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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2307.07526 | 44 | Scott W. Cunningham, Gareth H. Davies, Robert M. Davison, Rahul Dé, Denis Dennehy, Yanqing Duan, Rameshwar Dubey, Rohita Dwivedi, John S. Edwards, Carlos Flavián, Robin Gauld, Varun Grover, Mei-Chih Hu, Marijn Janssen, Paul Jones, Iris Junglas, Sangeeta Khorana, Sascha Kraus, Kai R. Larsen, Paul Latreille, Sven Laumer, F. Tegwen Malik, Abbas Mardani, Marcello Mariani, Sunil Mithas, Emmanuel Mogaji, Jeretta Horn Nord, Siobhan OâConnor, Fevzi Okumus, Margherita Pagani, Neeraj Pandey, Savvas Papagiannidis, Ilias O. Pappas, Nishith Pathak, Jan Pries-Heje, Ramakrishnan Raman, Nripendra P. Rana, Sven-Volker Rehm, Samuel Ribeiro-Navarrete, Alexander Richter, Frantz Rowe, Suprateek Sarker, Bernd Carsten Stahl, Manoj Kumar Tiwari, Wil van der Aalst, | 2307.07526#44 | Can I say, now machines can think? | Generative AI techniques have opened the path for new generations of machines
in diverse domains. These machines have various capabilities for example, they
can produce images, generate answers or stories, and write codes based on the
"prompts" only provided by users. These machines are considered 'thinking
minds' because they have the ability to generate human-like responses. In this
study, we have analyzed and explored the capabilities of artificial
intelligence-enabled machines. We have revisited on Turing's concept of
thinking machines and compared it with recent technological advancements. The
objections and consequences of the thinking machines are also discussed in this
study, along with available techniques to evaluate machines' cognitive
capabilities. We have concluded that Turing Test is a critical aspect of
evaluating machines' ability. However, there are other aspects of intelligence
too, and AI machines exhibit most of these aspects. | http://arxiv.org/pdf/2307.07526 | Nitisha Aggarwal, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir | cs.AI, cs.CY, I.2.m Miscellaneous | 11 pages, 3 figures | null | cs.AI | 20230711 | 20230711 | [
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2307.04964 | 45 | Figure 5: Left shows an equivalent structure to the RLHF framework in Figure 1. Right shows an implementation detail list for PPO. The number with circle indicates where this strategy is used in the PPO training. The pentagram indicates the method used by PPO-max.
Figure 4. We observed stable convergence processes in training loss, but higher rewards do not reï¬ect better policy behaviors from the perspective of human and GPT-4 evaluation. This means that the reward scores and training losses do not indicate whether the PPO is optimizing correctly. In vanilla PPO training, the response rewards of policy model gradually deviate from the original distribution and exhibit long-tail characteristics. We show the distribution of response rewards under different training steps in the Appendix A. | 2307.04964#45 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.04986 | 45 | Method name __init__() get_health_string() ask_agent_stay_at_home() get_response_and_reasoning() decide_location() add_agent_interaction() interact() infect() prepare_step() step() Method description Initializes the Citizen object by setting its attributes Returns a descriptive string representing the citizen's health condition based on the number of days infected. It provides different health descriptions for different infection stages Checks if response provided by LLM is a âyesâ or ânoâ and returns True and False respectively Provides a prompt to LLM and parses the output. Returns response and reasoning by the LLM If ask_agent_stay_at_home() returns True, agentâs location is set to âhomeâ else it is set to âgridâ Creates a list of agents for interaction in the world Makes agents interact with other agents in the agentâs agent_interaction list Infects another agent with respect to a probability threshold and health status Prepares the agent for the eventual step() function by deciding on its location first Agent interacts with all the agents in their interaction list. Where Method is used __init__() of World Class get_response_and_reasoning() | 2307.04986#45 | Epidemic Modeling with Generative Agents | This study offers a new paradigm of individual-level modeling to address the
grand challenge of incorporating human behavior in epidemic models. Using
generative artificial intelligence in an agent-based epidemic model, each agent
is empowered to make its own reasonings and decisions via connecting to a large
language model such as ChatGPT. Through various simulation experiments, we
present compelling evidence that generative agents mimic real-world behaviors
such as quarantining when sick and self-isolation when cases rise.
Collectively, the agents demonstrate patterns akin to multiple waves observed
in recent pandemics followed by an endemic period. Moreover, the agents
successfully flatten the epidemic curve. This study creates potential to
improve dynamic system modeling by offering a way to represent human brain,
reasoning, and decision making. | http://arxiv.org/pdf/2307.04986 | Ross Williams, Niyousha Hosseinichimeh, Aritra Majumdar, Navid Ghaffarzadegan | cs.AI, cs.MA, econ.GN, nlin.AO, physics.soc-ph, q-fin.EC | null | null | cs.AI | 20230711 | 20230711 | [] |
2307.05300 | 45 | et al., 2023; Shinn et al., 2023; Gou et al., 2023; Chen et al., 2023; Huang et al., 2022; Yao et al., 2022). However, these methods do not necessarily reduce factual hallucination. Retrieval augmented LLMs (Borgeaud et al., 2022; Izacard et al., 2022; Wang et al., 2022; Shuster et al., 2021) enhance knowledge acquisition but do not improve rea- soning abilities. We propose Solo Performance Prompting (SPP) to elicit both knowledge and reasoning abilities in LLMs, improving factual- ity while maintaining strong performance on pure- reasoning tasks.
6 Conclusion Solo Performance Prompting unleashes the cogni- tive synergy abilities within powerful LLMs, sig- nificantly reducing factual hallucination while en- hancing reasoning. The performance is assessed using newly proposed tasks, e.g., Trivia Creative Writing and Codenames Collaborative, demonstrat- ing superior results compared to Standard, CoT and Self-Refine. The discovery of the emergent na- ture of cognitive synergy on different LLMs draws interesting analogy to human development.
# Limitations | 2307.05300#45 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.07526 | 45 | Alexander Richter, Frantz Rowe, Suprateek Sarker, Bernd Carsten Stahl, Manoj Kumar Tiwari, Wil van der Aalst, Viswanath Venkatesh, Giampaolo Viglia, Michael Wade, Paul Walton, Jochen Wirtz, and Ryan Wright. Opinion paper: âso what if chatgpt wrote it?â multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management, 71:102642, 2023. | 2307.07526#45 | Can I say, now machines can think? | Generative AI techniques have opened the path for new generations of machines
in diverse domains. These machines have various capabilities for example, they
can produce images, generate answers or stories, and write codes based on the
"prompts" only provided by users. These machines are considered 'thinking
minds' because they have the ability to generate human-like responses. In this
study, we have analyzed and explored the capabilities of artificial
intelligence-enabled machines. We have revisited on Turing's concept of
thinking machines and compared it with recent technological advancements. The
objections and consequences of the thinking machines are also discussed in this
study, along with available techniques to evaluate machines' cognitive
capabilities. We have concluded that Turing Test is a critical aspect of
evaluating machines' ability. However, there are other aspects of intelligence
too, and AI machines exhibit most of these aspects. | http://arxiv.org/pdf/2307.07526 | Nitisha Aggarwal, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir | cs.AI, cs.CY, I.2.m Miscellaneous | 11 pages, 3 figures | null | cs.AI | 20230711 | 20230711 | [
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2307.04964 | 46 | An empirical strategy is to compare the training process of good and bad policy models to ï¬nd suitable metrics. We show more indicative training metrics at the bottom of Figure 4, including perplexity, KL divergence between the policy and reference models, and the average length of generation responses. Previous work proposed an approximate linear relationship between the root KL and PM scores [17], but for smaller models, such an association appeared to be weak. We ï¬nd the model response falls into the OOD region of preference model when the original policy is over-optimized. We will further discuss this scaling effects in the next section. We simultaneously observe that the collapsed model uniformly delivers longer responses and exhibits lower perplexity for such generative patterns. We use these metrics to show the importance of different tricks and their impact on PPO training in section 5.3.
# Implement Details in PPO | 2307.04964#46 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 46 | # Limitations
Although Solo Performance Prompting exhibits promising improvements in acquiring factually cor- rect knowledge compared to Standard prompting, it has some limitations. For instance, even when a fine-grained persona is assigned, the answer may still be incorrect. It remains unclear to what ex- tent assigning a persona can help enhance domain knowledge in a specific area. Dedicated diagnostic experiments and theoretical efforts are needed to quantify the impact of having a persona or not.
Furthermore, we currently adopt an identical SPP prompt with the same two demonstration ex- amples for any given task inputs, which may be suboptimal. Future work investigating how to find better demonstration examples conditioned on each input could further improve the effectiveness of SPP.
Last but not least, if given sufficient computa- tional budget, a natural variant of SPP could extend to a multi-agent cognitive synergist setup where a leader persona identifies several expert agents and forms a cabinet to collaboratively solve a task. The multi-agent setup allows for leveraging richer computation power, larger local memory, and more flexible human-computer interaction, which could be essential for deploying to real-world applica- tions.
# References
2023. Auto-gpt. https://github.com/Significant- Gravitas/Auto-GPT. | 2307.05300#46 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.07526 | 46 | [17] Luciano Floridi. Ai as agency without intelligence: on chatgpt, large language models, and other generative models. Philosophy & Technology, 36(1):15, Mar 2023.
[18] Ian J. Deary, Lars Penke, and Wendy Johnson. The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3):201â211, Mar 2010.
[19] J. P. Guilford. The nature of human intelligence. The nature of human intelligence. McGraw-Hill, New York, NY, US, 1967.
[20] Heung-Yeung Shum, Xiaodong He, and Di Li. From eliza to xiaoice: Challenges and opportunities with social
# chatbots. arXiv preprint arXiv:1801.01957, 2018. the
[21] What turing. what-comes-after-the-turing-test. Accessed: 2023-05-10. comes after https://www.newyorker.com/tech/annals-of-technology/
[22] Richard Byrne. The Thinking Ape: Evolutionary Origins of Intelligence. Oxford University Press, 02 1995. [23] James R. Flynn. What is intelligence? Beyond the Flynn effect. What is intelligence? Beyond the Flynn effect.
Cambridge University Press, New York, NY, US, 2007. | 2307.07526#46 | Can I say, now machines can think? | Generative AI techniques have opened the path for new generations of machines
in diverse domains. These machines have various capabilities for example, they
can produce images, generate answers or stories, and write codes based on the
"prompts" only provided by users. These machines are considered 'thinking
minds' because they have the ability to generate human-like responses. In this
study, we have analyzed and explored the capabilities of artificial
intelligence-enabled machines. We have revisited on Turing's concept of
thinking machines and compared it with recent technological advancements. The
objections and consequences of the thinking machines are also discussed in this
study, along with available techniques to evaluate machines' cognitive
capabilities. We have concluded that Turing Test is a critical aspect of
evaluating machines' ability. However, there are other aspects of intelligence
too, and AI machines exhibit most of these aspects. | http://arxiv.org/pdf/2307.07526 | Nitisha Aggarwal, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir | cs.AI, cs.CY, I.2.m Miscellaneous | 11 pages, 3 figures | null | cs.AI | 20230711 | 20230711 | [
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2307.04964 | 47 | # Implement Details in PPO
We propose the instability and pattern collapse problem of the primitive PPO algorithm in sec 5.2. Such sensitivity derives from the over-optimization of the policy model which traps it into ï¬xed generative patterns. Recent works have explored the implementation details of PPO algorithms in different scenarios. However, the application scenarios and data structures of traditional RL are quite different from RLHF. We determined to verify the applicability of these tricks in language model training and propose a set of PPO implementations that support stable optimization. We mainly focus on methods that efï¬ciently assist PPO training and their parameter sensitivity in the body of this paper. Figure 5 illustrates numerous available tricks in PPO training, we ï¬rst summarize the score reparameterization method (§5.3.1), followed by the optimization constraints for policy model (§5.3.2), and ï¬nally we present the different initialization methods for policy and critic models (§5.3.3). More experiments on hyper-parameter tuning and tricks that are veriï¬ed as less critical
12 | 2307.04964#47 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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}
] |
2307.04986 | 47 | 18
# Table S4. Algorithm for a day in the World
Step Number 1 2 3 4 5 6 7 8 Description Initialize the simulation by creating the world and its agents At the beginning of each time step, agents independently decide whether to stay home or go outside For agents who decide to go outside, calculate potential interaction partners by identifying agents also present on grid Allow agents to interact with their potential partners, potentially infecting them based on the infection_rate and health_condition Update the state of the agents and the world based on their actions and interactions during the day Calculate the day_infected_is_4 by tallying the number of agents who have reached the fourth day of their infection Save the entire world, including agents and their attributes, into a local file as a checkpoint at the end of each time step Check for an early stopping condition: if there are no infected agents for two consecutive days, save the final checkpoint and halt the simulation Provide the day_infected_is_4 as feedback to agents at the beginning of the next time step 9
19
Table S5. World class attributes | 2307.04986#47 | Epidemic Modeling with Generative Agents | This study offers a new paradigm of individual-level modeling to address the
grand challenge of incorporating human behavior in epidemic models. Using
generative artificial intelligence in an agent-based epidemic model, each agent
is empowered to make its own reasonings and decisions via connecting to a large
language model such as ChatGPT. Through various simulation experiments, we
present compelling evidence that generative agents mimic real-world behaviors
such as quarantining when sick and self-isolation when cases rise.
Collectively, the agents demonstrate patterns akin to multiple waves observed
in recent pandemics followed by an endemic period. Moreover, the agents
successfully flatten the epidemic curve. This study creates potential to
improve dynamic system modeling by offering a way to represent human brain,
reasoning, and decision making. | http://arxiv.org/pdf/2307.04986 | Ross Williams, Niyousha Hosseinichimeh, Aritra Majumdar, Navid Ghaffarzadegan | cs.AI, cs.MA, econ.GN, nlin.AO, physics.soc-ph, q-fin.EC | null | null | cs.AI | 20230711 | 20230711 | [] |
2307.05300 | 47 | # References
2023. Auto-gpt. https://github.com/Significant- Gravitas/Auto-GPT.
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wen- liang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al. 2023. A multi- task, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023.
Sebastian Borgeaud, Arthur Mensch, Jordan Hoff- mann, Trevor Cai, Eliza Rutherford, Katie Milli- can, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. 2022. Improving language models by retrieving from tril- lions of tokens. In International conference on ma- chine learning, pages 2206â2240. PMLR.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877â1901. | 2307.05300#47 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.07526 | 47 | Cambridge University Press, New York, NY, US, 2007.
[24] Yuval Noah Harari. 21 Lessons for the 21st Century:âTruly mind-expanding... Ultra-topicalâGuardian. Random House, 2018.
[25] The chatgpt king isnât worried, but he knows you might be. https://www.nytimes.com/2023/03/31/
technology/sam-altman-open-ai-chatgpt.html. Accessed: 2023-04-15. his
[26] The âitâs godfather-artificial-intelligence-geoffrey-hinton-climate-change/. Accessed: 2023-05-22.
https://www.sciencefocus.com/ future-technology/ai-has-dominated-chess-for-25-years-but-now-it-wants-to-lose/. Ac- cessed: 2023-04-16.
9
Can I say, now machines can think?
[28] Yash Raj Shrestha, Shiko M Ben-Menahem, and Georg Von Krogh. Organizational decision-making structures in the age of artiï¬cial intelligence. California management review, 61(4):66â83, 2019. | 2307.07526#47 | Can I say, now machines can think? | Generative AI techniques have opened the path for new generations of machines
in diverse domains. These machines have various capabilities for example, they
can produce images, generate answers or stories, and write codes based on the
"prompts" only provided by users. These machines are considered 'thinking
minds' because they have the ability to generate human-like responses. In this
study, we have analyzed and explored the capabilities of artificial
intelligence-enabled machines. We have revisited on Turing's concept of
thinking machines and compared it with recent technological advancements. The
objections and consequences of the thinking machines are also discussed in this
study, along with available techniques to evaluate machines' cognitive
capabilities. We have concluded that Turing Test is a critical aspect of
evaluating machines' ability. However, there are other aspects of intelligence
too, and AI machines exhibit most of these aspects. | http://arxiv.org/pdf/2307.07526 | Nitisha Aggarwal, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir | cs.AI, cs.CY, I.2.m Miscellaneous | 11 pages, 3 figures | null | cs.AI | 20230711 | 20230711 | [
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2307.04964 | 48 | 12
16 | ââ reward scale 500 â reward normalize, 6 = 0.3 14 | â reward normalize, A, advantage normalize, A Y 400 12 advantage normalize, 6 = 0.12 D Bio | g y 300 ze 5 a 6 w 200 [v4 4 2 100 i} 200 400 600 800 1000 i} 200 400 600 800 1000 Step Step 0.30 1.025 0.25 0.20 1.020 2 0.15 3 1.015 a e 0.10 5 © 1.010 0.05 0.00 1.005 0.05 i} 200 400 600 800 1000 i} 200 400 600 800 1000 Step Step
w & 5 & o 2 A 2 <
Figure 6: We show the variation of training metrics when constraining the ï¬uctuations of intermediate variables. δ indicates the clipped range, the KL divergence indicates the optimization magnitude of policy model, and the perplexity indicates the uncertainty of policy model for current response. Scaling or clipping strategy for reward and advantage contributes to the training stability compared to vanilla PPO. Temporarily stable settings, such as reward normalize with δ = 0.3, also exhibit consistent upward trends across metrics, which implies that pattern collapse problems likewise occur when training longer. | 2307.04964#48 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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"id": "2112.00861"
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2307.04986 | 48 | Attribute Name initial_healthy initial_infected contact_rate step_count offset name height, width grid current_date total_contact_rates track_contact_rate day_infected_is_4 list_new_cases daily_new_cases infected agents_on_grid max_potential_interactions schedule datacollector Attribute Description The initial number of healthy (susceptible) agents The initial number of infected agents The maximum number of interactions an agent can have per step The number of time steps to simulate Helper variable to enable loading checkpoints Name for saving checkpoints and simulation outputs Dimensions of the world grid The grid where agents exist. This is functionally not used The current date in the simulation. This has no functionality, but is used simply for logging purposes Stores the total number of contacts in each time step List that tracks the total contact rate over time steps List to track the number of agents infected for 4 days List to store the number of new cases for each day The number of new cases for the current day. It is initially set equal to the number of agents initially infected (initial_infected) The total number of infected agents List of agents currently on the grid Maximum number of potential interactions an agent can have with other agents who are also on grid Scheduler for agent activation. This is provided by Mesa | 2307.04986#48 | Epidemic Modeling with Generative Agents | This study offers a new paradigm of individual-level modeling to address the
grand challenge of incorporating human behavior in epidemic models. Using
generative artificial intelligence in an agent-based epidemic model, each agent
is empowered to make its own reasonings and decisions via connecting to a large
language model such as ChatGPT. Through various simulation experiments, we
present compelling evidence that generative agents mimic real-world behaviors
such as quarantining when sick and self-isolation when cases rise.
Collectively, the agents demonstrate patterns akin to multiple waves observed
in recent pandemics followed by an endemic period. Moreover, the agents
successfully flatten the epidemic curve. This study creates potential to
improve dynamic system modeling by offering a way to represent human brain,
reasoning, and decision making. | http://arxiv.org/pdf/2307.04986 | Ross Williams, Niyousha Hosseinichimeh, Aritra Majumdar, Navid Ghaffarzadegan | cs.AI, cs.MA, econ.GN, nlin.AO, physics.soc-ph, q-fin.EC | null | null | cs.AI | 20230711 | 20230711 | [] |
2307.05300 | 48 | Sébastien Bubeck, Varun Chandrasekaran, Ronen El- dan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lund- berg, et al. 2023. Sparks of artificial general intelli- gence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712.
Tianle Cai, Xuezhi Wang, Tengyu Ma, Xinyun Chen, and Denny Zhou. 2023. Large language models as tool makers. arXiv preprint arXiv:2305.17126.
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2023. Teaching large language models to self-debug. arXiv preprint arXiv:2304.05128.
Petru L Cur¸seu, Nicoleta Meslec, Helen Pluut, and Ger- ardus JM Lucas. 2015. Cognitive synergy in groups and group-to-individual transfer of decision-making competencies. Frontiers in psychology, 6:1375. | 2307.05300#48 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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"id": "2302.04023"
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] |
2307.07526 | 48 | [29] G Jefferson. The mind of mechanical man. Br Med J, 1(4616):1105â1110, June 1949. [30] The
by 20230224-the-ai-emotions-dreamed-up-by-chatgpt. Accessed: 2023-04-17. ai emotions dreamed up chatgpt. https://www.bbc.com/future/article/
[31] A conversation with bingâs chatbot left me deeply unsettled. https://www.nytimes.com/2023/02/16/ technology/bing-chatbot-microsoft-chatgpt.html. Accessed: 2023-03-20.
[32] M Berg-Weger and J E Morley. Editorial: Loneliness and social isolation in older adults during the COVID-19 pandemic: Implications for gerontological social work. J Nutr Health Aging, 24(5):456â458, 2020.
[33] Hyesun Choung, Prabu David, and Arun Ross. Trust in ai and its role in the acceptance of ai technologies. | 2307.07526#48 | Can I say, now machines can think? | Generative AI techniques have opened the path for new generations of machines
in diverse domains. These machines have various capabilities for example, they
can produce images, generate answers or stories, and write codes based on the
"prompts" only provided by users. These machines are considered 'thinking
minds' because they have the ability to generate human-like responses. In this
study, we have analyzed and explored the capabilities of artificial
intelligence-enabled machines. We have revisited on Turing's concept of
thinking machines and compared it with recent technological advancements. The
objections and consequences of the thinking machines are also discussed in this
study, along with available techniques to evaluate machines' cognitive
capabilities. We have concluded that Turing Test is a critical aspect of
evaluating machines' ability. However, there are other aspects of intelligence
too, and AI machines exhibit most of these aspects. | http://arxiv.org/pdf/2307.07526 | Nitisha Aggarwal, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir | cs.AI, cs.CY, I.2.m Miscellaneous | 11 pages, 3 figures | null | cs.AI | 20230711 | 20230711 | [
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2307.04964 | 49 | are discussed in the appendix, such as advantage estimation function and gradient clipping. In the following, it always refers to our own experiments when we mention PPO if not speciï¬cally stated.
# 5.3.1 Score Reparameterization
We use the term âscoreâ to refer to the two vital intermediate variables involved in PPO training. The reward score is given by the reward model trained with human preferences data, and the advantage score is calculated by the GAE function. According to existing works, reparameterizing these scores to a stable distribution (e.g., a standard normal distribution) may intensify the stability of PPO. The reported operations are into three parts for verification. We use {r (x, y)} © {rn (x, y)}8_, to denote a reward sequence in training, 1, («, y) to denote the results of per-batch reward, o(A) and A to denote the mean and standard deviation of variable A. Comparative experiments with different tricks and hyperparameters are shown in Figure[6] | 2307.04964#49 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.04986 | 49 | agents List of agents currently on the grid Maximum number of potential interactions an agent can have with other agents who are also on grid Scheduler for agent activation. This is provided by Mesa (mesa.time.RandomActivation) Collects data during the simulation. This is provided by Mesa (mesa.DataCollector) Attribute State Static Static Static Static Static Static Static Static Dynamic Dynamic Dynamic Dynamic Dynamic Dynamic Dynamic Dynamic Dynamic Static Dynamic | 2307.04986#49 | Epidemic Modeling with Generative Agents | This study offers a new paradigm of individual-level modeling to address the
grand challenge of incorporating human behavior in epidemic models. Using
generative artificial intelligence in an agent-based epidemic model, each agent
is empowered to make its own reasonings and decisions via connecting to a large
language model such as ChatGPT. Through various simulation experiments, we
present compelling evidence that generative agents mimic real-world behaviors
such as quarantining when sick and self-isolation when cases rise.
Collectively, the agents demonstrate patterns akin to multiple waves observed
in recent pandemics followed by an endemic period. Moreover, the agents
successfully flatten the epidemic curve. This study creates potential to
improve dynamic system modeling by offering a way to represent human brain,
reasoning, and decision making. | http://arxiv.org/pdf/2307.04986 | Ross Williams, Niyousha Hosseinichimeh, Aritra Majumdar, Navid Ghaffarzadegan | cs.AI, cs.MA, econ.GN, nlin.AO, physics.soc-ph, q-fin.EC | null | null | cs.AI | 20230711 | 20230711 | [] |
2307.05300 | 49 | Ameet Deshpande, Vishvak Murahari, Tanmay Rajpuro- hit, Ashwin Kalyan, and Karthik Narasimhan. 2023. Toxicity in chatgpt: Analyzing persona-assigned lan- guage models. arXiv preprint arXiv:2304.05335.
Yao Fu, Hao Peng, Tushar Khot, and Mirella Lapata. 2023. Improving language model negotiation with self-play and in-context learning from ai feedback. arXiv preprint arXiv:2305.10142.
Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot. 2022. Complexity-based prompt- arXiv preprint ing for multi-step reasoning. arXiv:2210.00720.
Ben Goertzel. 2009. Cognitive synergy: A universal principle for feasible general intelligence. In 2009 8th IEEE International Conference on Cognitive In- formatics, pages 464â468. IEEE.
Ben Goertzel. 2017. A formal model of cognitive syn- ergy. In Artificial General Intelligence: 10th Interna- tional Conference, AGI 2017, Melbourne, VIC, Aus- tralia, August 15-18, 2017, Proceedings 10, pages 13â22. Springer. | 2307.05300#49 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.07526 | 49 | [33] Hyesun Choung, Prabu David, and Arun Ross. Trust in ai and its role in the acceptance of ai technologies.
International Journal of HumanâComputer Interaction, 39(9):1727â1739, 2023. [34] H. Holden Thorp. Chatgpt is fun, but not an author. Science, 379(6630):313â313, 2023. [35] Graham Oppy and David Dowe. The Turing Test. In Edward N. Zalta, editor, The Stanford Encyclopedia of
Philosophy. Metaphysics Research Lab, Stanford University, Winter 2021 edition, 2021.
[36] Jonathan M Garibaldi. The need for fuzzy ai. IEEE/CAA Journal of Automatica Sinica, 6(3):610â622, 2019. [37] Zhi-Hua Zhou. Machine learning. Springer Nature, 2021. [38] Marija Brankovi´c. Who believes in ESP: Cognitive and motivational determinants of the belief in Extra-Sensory
perception. Eur J Psychol, 15(1):120â139, February 2019.
[39] Luciano Floridi and Massimo Chiriatti. Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines,
30(4):681â694, Dec 2020. can | 2307.07526#49 | Can I say, now machines can think? | Generative AI techniques have opened the path for new generations of machines
in diverse domains. These machines have various capabilities for example, they
can produce images, generate answers or stories, and write codes based on the
"prompts" only provided by users. These machines are considered 'thinking
minds' because they have the ability to generate human-like responses. In this
study, we have analyzed and explored the capabilities of artificial
intelligence-enabled machines. We have revisited on Turing's concept of
thinking machines and compared it with recent technological advancements. The
objections and consequences of the thinking machines are also discussed in this
study, along with available techniques to evaluate machines' cognitive
capabilities. We have concluded that Turing Test is a critical aspect of
evaluating machines' ability. However, there are other aspects of intelligence
too, and AI machines exhibit most of these aspects. | http://arxiv.org/pdf/2307.07526 | Nitisha Aggarwal, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir | cs.AI, cs.CY, I.2.m Miscellaneous | 11 pages, 3 figures | null | cs.AI | 20230711 | 20230711 | [
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2307.04964 | 50 | Reward Scaling controls training ï¬uctuations by scaling the rewards where the rewards are divided by the standard deviation of a rolling discounted sum. Based on the observation history, the reward for current state can be expressed as rn (x, y) /Ï(r (x, y)). In contrast to the experimental results of Engstrom [29], we show that reward scaling doesnât guide proper policy optimization, and PPO exhibits consistent patterns in training trajectories with and without reward scaling. In our experiments, we believe that tighter constraints are required to ensure training stability.
Reward Normalization and Clipping was ï¬rst proposed by Mnih [36]. The processed reward can be denoted as:
Ër (x, y) = clip rn (x, y) â r (x, y) Ï(r (x, y) , âδ, δ , (18)
13 | 2307.04964#50 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.04986 | 50 | 20
Table S6. World class methods
Method Name __init__() distribute_agents() decide_agent_interactions() step() run_model() save_checkpoint() load_checkpoint() Method description Initializes the World object and sets its attributes. It also creates the grid, initializes agents, and sets up the data collector. Distributes agents on the grid randomly. Determines the interaction partners for each agent based on the contact rate. The goal is to ensure that interactions between agents are a two-way process Includes important steps such as agent preparations, determining interaction partners, calculating the total contact rate, executing individual agent steps, and updating agent attributes and global infection statistics Runs the model for the specified number of time steps. It collects data, performs model steps, saves checkpoints, and prints relevant information Saves a checkpoint of the model to the specified file path Loads a checkpoint from the specified file path Called when an instance is created of the World class __init__() function of the World class step() function of World class run_model() of the World class Called in the script that runs the simulation
21 | 2307.04986#50 | Epidemic Modeling with Generative Agents | This study offers a new paradigm of individual-level modeling to address the
grand challenge of incorporating human behavior in epidemic models. Using
generative artificial intelligence in an agent-based epidemic model, each agent
is empowered to make its own reasonings and decisions via connecting to a large
language model such as ChatGPT. Through various simulation experiments, we
present compelling evidence that generative agents mimic real-world behaviors
such as quarantining when sick and self-isolation when cases rise.
Collectively, the agents demonstrate patterns akin to multiple waves observed
in recent pandemics followed by an endemic period. Moreover, the agents
successfully flatten the epidemic curve. This study creates potential to
improve dynamic system modeling by offering a way to represent human brain,
reasoning, and decision making. | http://arxiv.org/pdf/2307.04986 | Ross Williams, Niyousha Hosseinichimeh, Aritra Majumdar, Navid Ghaffarzadegan | cs.AI, cs.MA, econ.GN, nlin.AO, physics.soc-ph, q-fin.EC | null | null | cs.AI | 20230711 | 20230711 | [] |
2307.05300 | 50 | Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Nan Duan, and Weizhu Chen. 2023. Critic: Large language models can self-correct with tool-interactive critiquing.
Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tomp- son, Igor Mordatch, Yevgen Chebotar, et al. 2022. Inner monologue: Embodied reasoning through arXiv preprint planning with language models. arXiv:2207.05608.
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lu- cas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2022. Few-shot learning with re- trieval augmented language models. arXiv preprint arXiv:2208.03299. | 2307.05300#50 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.07526 | 50 | 30(4):681â694, Dec 2020. can
[40] Ai that learn the patterns of human language. https://news.mit.edu/2022/ ai-learn-patterns-language-0830. Accessed: 2023-02-25.
[41] Selmer Bringsjord, Paul Bello, and David Ferrucci. Creativity, the turing test, and the (better) lovelace test. The Turing test: the elusive standard of artiï¬cial intelligence, pages 215â239, 2003.
[42] Mark O. Riedl. The lovelace 2.0 test of artiï¬cial creativity and intelligence. arXiv preprint arXiv:1410.6142, 2014.
[43] David MW Powers. The total turing test and the loebner prize. In New Methods in Language Processing and Computational Natural Language Learning, 1998.
[44] Henry S Baird, Allison L Coates, and Richard J Fateman. Pessimalprint: a reverse turing test. International Journal on Document Analysis and Recognition, 5:158â163, 2003.
[45] Abhimanyu S Ahuja. The impact of artiï¬cial intelligence in medicine on the future role of the physician. PeerJ, 7:e7702, 2019. | 2307.07526#50 | Can I say, now machines can think? | Generative AI techniques have opened the path for new generations of machines
in diverse domains. These machines have various capabilities for example, they
can produce images, generate answers or stories, and write codes based on the
"prompts" only provided by users. These machines are considered 'thinking
minds' because they have the ability to generate human-like responses. In this
study, we have analyzed and explored the capabilities of artificial
intelligence-enabled machines. We have revisited on Turing's concept of
thinking machines and compared it with recent technological advancements. The
objections and consequences of the thinking machines are also discussed in this
study, along with available techniques to evaluate machines' cognitive
capabilities. We have concluded that Turing Test is a critical aspect of
evaluating machines' ability. However, there are other aspects of intelligence
too, and AI machines exhibit most of these aspects. | http://arxiv.org/pdf/2307.07526 | Nitisha Aggarwal, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir | cs.AI, cs.CY, I.2.m Miscellaneous | 11 pages, 3 figures | null | cs.AI | 20230711 | 20230711 | [
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2307.04964 | 51 | 13
5.5 220 5.0 200 4.5 ⬠180 2 Pao & 160 g o 3.5 2140 g 5 3.0 # 120 â KL penalty âa 2.5 â Entropy Bonus 100 20 ââ Importance Sample . ââ KL Penalty+Importance Sample 80 0 200 400 600 800 0 200 400 600 800 Step Step 1.0250 1.0225 ie 1.0200 5 Fa) & 1.0175 5 co] 2 2 z 5 1.0150 Zz 1.0125 1.0100 1.0075 0 200 400 600 800 0 200 400 600 800 Step Step
Figure 7: Training dynamics when using different methods to constrain the policy optimization. We show that all modiï¬cations can induce convergence, but only a penalty of the policy entropy or KL divergence can provide a long-lasting stable optimization. It is worth noting that all methods ( including those shown in Sec 5.3.1) exhibit consistent variation in response length and perplexity in the early training period, which may imply some bias in the reward model preference. | 2307.04964#51 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.04986 | 51 | 21
References 1. Behaviour fuels, and fights, pandemics. Nature Human Behaviour. 2020;4(5):435-. 2. Kraemer MUG, Yang C-H, Gutierrez B, Wu C-H, Klein B, Pigott DM, et al. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science. 2020;368(6490):493-7.
3. Rahmandad H, Xu R, Ghaffarzadegan N. A missing behavioural feedback in COVID-19 models is the key to several puzzles. BMJ Global Health. 2022;7(10):e010463.
4. Ferguson N. Capturing human behaviour. Nature. 2007;446(7137):733-. 5. Bauch C, T., Galvani A, P. Social Factors in Epidemiology. Science. 2013;342(6154):47-9. 6. Funk S, Salathé M, Jansen VAA. Modelling the influence of human behaviour on the spread of infectious diseases: a review. Journal of The Royal Society Interface. 2010;7(50):1247-56. 7. Bedson J, Skrip LA, Pedi D, Abramowitz S, Carter S, Jalloh MF, et al. A review and agenda | 2307.04986#51 | Epidemic Modeling with Generative Agents | This study offers a new paradigm of individual-level modeling to address the
grand challenge of incorporating human behavior in epidemic models. Using
generative artificial intelligence in an agent-based epidemic model, each agent
is empowered to make its own reasonings and decisions via connecting to a large
language model such as ChatGPT. Through various simulation experiments, we
present compelling evidence that generative agents mimic real-world behaviors
such as quarantining when sick and self-isolation when cases rise.
Collectively, the agents demonstrate patterns akin to multiple waves observed
in recent pandemics followed by an endemic period. Moreover, the agents
successfully flatten the epidemic curve. This study creates potential to
improve dynamic system modeling by offering a way to represent human brain,
reasoning, and decision making. | http://arxiv.org/pdf/2307.04986 | Ross Williams, Niyousha Hosseinichimeh, Aritra Majumdar, Navid Ghaffarzadegan | cs.AI, cs.MA, econ.GN, nlin.AO, physics.soc-ph, q-fin.EC | null | null | cs.AI | 20230711 | 20230711 | [] |
2307.05300 | 51 | Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017. TriviaQA: A large scale distantly supervised challenge dataset for reading comprehen- sion. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: Long Papers), pages 1601â1611, Vancouver, Canada. Association for Computational Linguistics.
Daniel Kahneman. 2011. Thinking, fast and slow. macmillan.
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yu- taka Matsuo, and Yusuke Iwasawa. 2022. Large lan- guage models are zero-shot reasoners. arXiv preprint arXiv:2205.11916.
Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023. Camel: Communicative agents for" mind" explo- ration of large scale language model society. arXiv preprint arXiv:2303.17760. | 2307.05300#51 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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] |
2307.07526 | 51 | [46] O. Bar et al. Impact of data on generalization of ai for surgical intelligence applications. Scientiï¬c reports, 10(1):22208, 2020.
[47] Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. Instruction tuning with gpt-4. arXiv preprint arXiv:2304.03277, 2023.
[48] Hanmeng Liu, Ruoxi Ning, Zhiyang Teng, Jian Liu, Qiji Zhou, and Yue Zhang. Evaluating the logical reasoning ability of chatgpt and gpt-4. arXiv preprint arXiv:2304.03439, 2023.
[49] D. M. Katz et al. Gpt-4 passes the bar exam. Available at SSRN 4389233, 2023. [50] Googleâs ai passed a famous test â and showed how the test is broken. https://www.washingtonpost.com/
technology/2022/06/17/google-ai-lamda-turing-test/. Accessed: 2023-05-17. | 2307.07526#51 | Can I say, now machines can think? | Generative AI techniques have opened the path for new generations of machines
in diverse domains. These machines have various capabilities for example, they
can produce images, generate answers or stories, and write codes based on the
"prompts" only provided by users. These machines are considered 'thinking
minds' because they have the ability to generate human-like responses. In this
study, we have analyzed and explored the capabilities of artificial
intelligence-enabled machines. We have revisited on Turing's concept of
thinking machines and compared it with recent technological advancements. The
objections and consequences of the thinking machines are also discussed in this
study, along with available techniques to evaluate machines' cognitive
capabilities. We have concluded that Turing Test is a critical aspect of
evaluating machines' ability. However, there are other aspects of intelligence
too, and AI machines exhibit most of these aspects. | http://arxiv.org/pdf/2307.07526 | Nitisha Aggarwal, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir | cs.AI, cs.CY, I.2.m Miscellaneous | 11 pages, 3 figures | null | cs.AI | 20230711 | 20230711 | [
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] |
2307.04964 | 52 | where δ denotes the clip region. It is generally believed In traditional RL that reward clip is ineffective or even detrimental in certain scenarios [29]. However, we ï¬nd that strict advantage cropping can also maintain training stability within a ï¬xed epoch. Interestingly, hyperparameter tuning does not affect the similarity of the different methods in the early training period, and models with larger clipping thresholds exhibit greater strategy alteration and converge to higher rewards in the latter half. As we mentioned earlier, this does not imply better performance in the manual evaluation. Determining the optimal clipping bound within a limited number of trials is challenging in view of such inconsistency between the reward model and manual evaluation results, we suggest adopting a relaxed clipping strategy and incorporating other tricks to constrain the policy optimization when training RLHF. | 2307.04964#52 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.04986 | 52 | for integrated disease models including social and behavioural factors. Nature Human Behaviour. 2021;5(7):834-46.
8. Xu R, Rahmandad H, Gupta M, DiGennaro C, Ghaffarzadegan N, Amini H, et al. Weather, air pollution, and SARS-CoV-2 transmission: a global analysis. The Lancet Planetary Health. 2021;5(10):e671-e80.
9. Rahmandad H, Lim TY, Sterman J. Behavioral dynamics of COVID-19: estimating underreporting, multiple waves, and adherence fatigue across 92 nations. System Dynamics Review. 2021;37(1):5-31.
10. Rahmandad H, Xu R, Ghaffarzadegan N. Enhancing Long-term Forecasting: Learning from COVID-19 Models. PLOS Computational Biology. 2022.
11. Rahmandad H. Behavioral Responses to Risk Promote Vaccinating High-contact Individuals First. medRxiv. 2021:2021.02.05.21251215.
12. Funk S, Bansal S, Bauch CT, Eames KTD, Edmunds WJ, Galvani AP, et al. Nine challenges in incorporating the dynamics of behaviour in infectious diseases models. Epidemics. 2015;10:21-5. | 2307.04986#52 | Epidemic Modeling with Generative Agents | This study offers a new paradigm of individual-level modeling to address the
grand challenge of incorporating human behavior in epidemic models. Using
generative artificial intelligence in an agent-based epidemic model, each agent
is empowered to make its own reasonings and decisions via connecting to a large
language model such as ChatGPT. Through various simulation experiments, we
present compelling evidence that generative agents mimic real-world behaviors
such as quarantining when sick and self-isolation when cases rise.
Collectively, the agents demonstrate patterns akin to multiple waves observed
in recent pandemics followed by an endemic period. Moreover, the agents
successfully flatten the epidemic curve. This study creates potential to
improve dynamic system modeling by offering a way to represent human brain,
reasoning, and decision making. | http://arxiv.org/pdf/2307.04986 | Ross Williams, Niyousha Hosseinichimeh, Aritra Majumdar, Navid Ghaffarzadegan | cs.AI, cs.MA, econ.GN, nlin.AO, physics.soc-ph, q-fin.EC | null | null | cs.AI | 20230711 | 20230711 | [] |
2307.05300 | 52 | Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2023. Self-refine: Iterative refinement with self-feedback. arXiv preprint arXiv:2303.17651.
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020. On faithfulness and factu- ality in abstractive summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1906â1919, On- line. Association for Computational Linguistics.
Theo X Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, and Armando Solar-Lezama. 2023. Demystifying gpt self-repair for code genera- tion. arXiv preprint arXiv:2306.09896.
OpenAI. 2023a. Gpt-35. https://platform.openai.com/docs/models/gpt-3-5.
OpenAI. 2023b. Gpt-4 technical report. | 2307.05300#52 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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"id": "2302.04023"
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"id": "2304.03442"
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"id": "2207.05608"
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"id": "2303.12712"
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{
"id": "2303.11366"
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{
"id": "2304.05128"
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{
"id": "2303.17651"
}
] |
2307.07526 | 52 | technology/2022/06/17/google-ai-lamda-turing-test/. Accessed: 2023-05-17.
[51] Robert Hanna. How and why chatgpt failed the turing test. Unpublished MS. Available online at URL=< https://www. academia. edu/94870578/How_and_Why_ChatGPT_Failed_The_Turing_Test_January_2023_version_, 2023.
[52] Ragnar Fjelland. Why general artiï¬cial intelligence will not be realized. Humanities and Social Sciences Communications, 7(1):1â9, 2020.
[53] Thilo Hagendorff. Ai ethics and its pitfalls: not living up to its own standards? AI and Ethics, 3(1):329â336, 2023. [54] S. Soatto et al. Taming ai bots: Controllability of neural states in large language models. arXiv preprint
arXiv:2305.18449, 2023.
[55] Michael Brady. Artiï¬cial intelligence and robotics. Artiï¬cial intelligence, 26(1):79â121, 1985.
10
Can I say, now machines can think? | 2307.07526#52 | Can I say, now machines can think? | Generative AI techniques have opened the path for new generations of machines
in diverse domains. These machines have various capabilities for example, they
can produce images, generate answers or stories, and write codes based on the
"prompts" only provided by users. These machines are considered 'thinking
minds' because they have the ability to generate human-like responses. In this
study, we have analyzed and explored the capabilities of artificial
intelligence-enabled machines. We have revisited on Turing's concept of
thinking machines and compared it with recent technological advancements. The
objections and consequences of the thinking machines are also discussed in this
study, along with available techniques to evaluate machines' cognitive
capabilities. We have concluded that Turing Test is a critical aspect of
evaluating machines' ability. However, there are other aspects of intelligence
too, and AI machines exhibit most of these aspects. | http://arxiv.org/pdf/2307.07526 | Nitisha Aggarwal, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir | cs.AI, cs.CY, I.2.m Miscellaneous | 11 pages, 3 figures | null | cs.AI | 20230711 | 20230711 | [
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2307.04964 | 53 | Advantages Normalization and Clipping has similarities to the operation on reward, but differs in details that its normalization occurs only at the minibatch level. After calculating the advantage based on GAE, PPO normalizes the advantage value by subtracting its mean and dividing it by its standard deviation. Andrychowicz [28] ï¬rst attempt to apply Advantages Normalization in gaming domain and reported that this trick didnât exhibit signiï¬cant improvements. Although parameter selection for advantage clipping would be more sensitive and difï¬cult, we instead ï¬nd that a severe constraint on advantage can provide similar effects to reward clip in PPO training. Considering that different score reparameterization operations theoretically provide similar effects on PPO training, we recommend constraining the instability of policy optimization on the reward level. Experiments on the simultaneous application of reward, advantage, or value clipping operations are shown in Appendix B.1.
# 5.3.2 Policy Constraints
To tackle the over-optimization problem on the policy model, an intuitive solution is to constrain the policy optimization to a limited range. We validate various existing tricks to control the update of generation policy, such constraints are empirically proved to be necessary for longer training
14
procedures. Figure. 7 shows the inï¬uence of different constraint methods and hyperparameters on policy optimization. | 2307.04964#53 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.04986 | 53 | 13. Weston D, Ip A, Amlôt R. Examining the application of behaviour change theories in the context of infectious disease outbreaks and emergency response: a review of reviews. BMC Public Health. 2020;20(1):1483.
14. Verelst F, Willem L, Beutels P. Behavioural change models for infectious disease transmission: a systematic review (2010â2015). Journal of The Royal Society Interface. 2016;13(125):20160820.
15. Cooper G. Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence. Journal of Science Education and Technology. 2023;32(3):444-52.
16. Mondal S, Das S, Vrana VG. How to Bell the Cat? A Theoretical Review of Generative Artificial Intelligence towards Digital Disruption in All Walks of Life. Technologies. 2023;11(2):44.
17. Park JS, O'Brien JC, Cai CJ, Morris MR, Liang P, Bernstein MS. Generative Agents: Interactive Simulacra of Human Behavior. arxiv2023.
18. Cao Y, Li S, Liu Y, Yan Z, Dai Y, Yu PS, et al. A Comprehensive Survey of AI-Generated | 2307.04986#53 | Epidemic Modeling with Generative Agents | This study offers a new paradigm of individual-level modeling to address the
grand challenge of incorporating human behavior in epidemic models. Using
generative artificial intelligence in an agent-based epidemic model, each agent
is empowered to make its own reasonings and decisions via connecting to a large
language model such as ChatGPT. Through various simulation experiments, we
present compelling evidence that generative agents mimic real-world behaviors
such as quarantining when sick and self-isolation when cases rise.
Collectively, the agents demonstrate patterns akin to multiple waves observed
in recent pandemics followed by an endemic period. Moreover, the agents
successfully flatten the epidemic curve. This study creates potential to
improve dynamic system modeling by offering a way to represent human brain,
reasoning, and decision making. | http://arxiv.org/pdf/2307.04986 | Ross Williams, Niyousha Hosseinichimeh, Aritra Majumdar, Navid Ghaffarzadegan | cs.AI, cs.MA, econ.GN, nlin.AO, physics.soc-ph, q-fin.EC | null | null | cs.AI | 20230711 | 20230711 | [] |
2307.05300 | 53 | OpenAI. 2023b. Gpt-4 technical report.
Joon Sung Park, Joseph C OâBrien, Carrie J Cai, Mered- ith Ringel Morris, Percy Liang, and Michael S Interactive Bernstein. 2023. Generative agents: arXiv preprint simulacra of human behavior. arXiv:2304.03442.
Anthony D Pellegrini. 2009. The role of play in human development. Oxford University Press, USA.
Jean Piaget. 1954. The construction of reality in the child.
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023. Is chatgpt a general-purpose natural language process- ing task solver? arXiv preprint arXiv:2302.06476.
Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, and Sebas- tian Riedel. 2022. Peer: A collaborative language model. arXiv preprint arXiv:2208.11663. | 2307.05300#53 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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"id": "2306.09896"
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{
"id": "2302.04023"
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"id": "2205.11916"
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"id": "2304.03442"
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"id": "2207.05608"
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"id": "2303.12712"
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] |
2307.07526 | 53 | 10
Can I say, now machines can think?
[56] Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando GSL Brandao, David A Buell, et al. Quantum supremacy using a programmable superconducting processor. Nature, 574(7779):505â510, 2019.
[57] Michael Broughton, Guillaume Verdon, Trevor McCourt, Antonio J Martinez, Jae Hyeon Yoo, Sergei V Isakov, Philip Massey, Ramin Halavati, Murphy Yuezhen Niu, Alexander Zlokapa, et al. Tensorï¬ow quantum: A software framework for quantum machine learning. arXiv preprint arXiv:2003.02989, 2020.
[58] V Van Roy et al. Ai and robotics innovation. Handbook of labor, human resources and population economics, pages 1â35, 2020.
[59] Jesús Retto. Sophia, ï¬rst citizen robot of the world. ResearchGate, URL: https://www. researchgate. net, 2017.
11 | 2307.07526#53 | Can I say, now machines can think? | Generative AI techniques have opened the path for new generations of machines
in diverse domains. These machines have various capabilities for example, they
can produce images, generate answers or stories, and write codes based on the
"prompts" only provided by users. These machines are considered 'thinking
minds' because they have the ability to generate human-like responses. In this
study, we have analyzed and explored the capabilities of artificial
intelligence-enabled machines. We have revisited on Turing's concept of
thinking machines and compared it with recent technological advancements. The
objections and consequences of the thinking machines are also discussed in this
study, along with available techniques to evaluate machines' cognitive
capabilities. We have concluded that Turing Test is a critical aspect of
evaluating machines' ability. However, there are other aspects of intelligence
too, and AI machines exhibit most of these aspects. | http://arxiv.org/pdf/2307.07526 | Nitisha Aggarwal, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir | cs.AI, cs.CY, I.2.m Miscellaneous | 11 pages, 3 figures | null | cs.AI | 20230711 | 20230711 | [
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"id": "2003.02989"
}
] |
2307.04964 | 54 | 14
procedures. Figure. 7 shows the inï¬uence of different constraint methods and hyperparameters on policy optimization.
Token Level KL-Penalty constrains the policy optimization by applying a regularization term to reward that is proportional to the KL-divergence of current and original policy distributions. This approach was ï¬rst introduced by Stiennon [25] and widely adopted in different RLHF implemen- tations. Given a template-response pair (x, y), we treat the logits distribution of the token output as a sampling of the policy distribution and apply an empirically estimated KL-penalty sequence to response reward, the total reward with KL-penalty can be denoted as:
rtotal(x, yi) = r(x, yi) â ηKL(ÏRL (yi|x), ÏSFT(yi|x)), (19) | 2307.04964#54 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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] |
2307.04986 | 54 | 18. Cao Y, Li S, Liu Y, Yan Z, Dai Y, Yu PS, et al. A Comprehensive Survey of AI-Generated
Content (AIGC): A History of Generative AI from GAN to ChatGPT. arxiv2023. 19. Metz C. OpenAI Plans to Up the Ante in Tech's A.I. Race. The New York Times. 2023
March 14, 2023.
22
20. Goldberg LR. An alternative "description of personality": The Big-Five factor structure. Journal of Personality and Social Psychology. 1990;59(6):1216-29.
21. Ghaffarzadegan N. Simulation-based what-if analysis for controlling the spread of Covid-19 in universities. PLOS ONE. 2021;16(2):e0246323.
22. Kermack WO, McKendrick AG, Walker GT. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London Series A, Containing Papers of a Mathematical and Physical Character. 1927;115(772):700-21. | 2307.04986#54 | Epidemic Modeling with Generative Agents | This study offers a new paradigm of individual-level modeling to address the
grand challenge of incorporating human behavior in epidemic models. Using
generative artificial intelligence in an agent-based epidemic model, each agent
is empowered to make its own reasonings and decisions via connecting to a large
language model such as ChatGPT. Through various simulation experiments, we
present compelling evidence that generative agents mimic real-world behaviors
such as quarantining when sick and self-isolation when cases rise.
Collectively, the agents demonstrate patterns akin to multiple waves observed
in recent pandemics followed by an endemic period. Moreover, the agents
successfully flatten the epidemic curve. This study creates potential to
improve dynamic system modeling by offering a way to represent human brain,
reasoning, and decision making. | http://arxiv.org/pdf/2307.04986 | Ross Williams, Niyousha Hosseinichimeh, Aritra Majumdar, Navid Ghaffarzadegan | cs.AI, cs.MA, econ.GN, nlin.AO, physics.soc-ph, q-fin.EC | null | null | cs.AI | 20230711 | 20230711 | [] |
2307.05300 | 54 | Noah Shinn, Beck Labash, and Ashwin Gopinath. 2023. Reflexion: an autonomous agent with dy- namic memory and self-reflection. arXiv preprint arXiv:2303.11366.
Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021. Retrieval augmentation reduces hallucination in conversation. arXiv preprint arXiv:2104.07567.
Steven A Sloman. 1996. The empirical case for two sys- tems of reasoning. Psychological bulletin, 119(1):3.
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. 2022. Beyond the imitation game: Quantifying and extrapolating the arXiv preprint capabilities of language models. arXiv:2206.04615. | 2307.05300#54 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 55 | θ where ÏRL (yi|x) denotes the action space of iâth reponse token, and η is a hyper-parameter. Anthropic [17] used a small weight to balance the ratio of reward and KL-penalty in PPO training (0.001), and they did not ï¬nd signiï¬cant effects of the above operation on RL training. Instead, we ï¬nd this constraint critical to the stability of PPO and allow further scaling up on the training step. Results with policy divergence penalty are illustrated in Figure 7 by setting lambda to 0.05, and there is a signiï¬cant difference to the method in Figure 6 with a noticeable correction in the later training period. Interestingly, we show that RLHF is able to signiï¬cantly improve the response quality while barely modifying the language modeling (exhibiting an almost zero KL divergence from the original policy). More experiments on the impact of different constraint values are shown in appendix B.2 | 2307.04964#55 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.04986 | 55 | 23. Qiu Z, Espinoza B, Vasconcelos VV, Chen C, Constantino SM, Crabtree SA, et al. Understanding the coevolution of mask wearing and epidemics: A network perspective. Proceedings of the National Academy of Sciences. 2022;119(26):e2123355119.
24. Rahmandad H, Sterman J. Quantifying the COVID-19 endgame: Is a new normal within reach? System Dynamics Review. 2022;38(4):329-53.
25. Richardson GP. Reflections on the foundations of system dynamics. System Dynamics Review. 2011;27(3):219-43.
26. Kazil J, Masad D, Crooks A, editors. Utilizing Python for Agent-Based Modeling: The Mesa Framework2020; Cham: Springer International Publishing.
23 | 2307.04986#55 | Epidemic Modeling with Generative Agents | This study offers a new paradigm of individual-level modeling to address the
grand challenge of incorporating human behavior in epidemic models. Using
generative artificial intelligence in an agent-based epidemic model, each agent
is empowered to make its own reasonings and decisions via connecting to a large
language model such as ChatGPT. Through various simulation experiments, we
present compelling evidence that generative agents mimic real-world behaviors
such as quarantining when sick and self-isolation when cases rise.
Collectively, the agents demonstrate patterns akin to multiple waves observed
in recent pandemics followed by an endemic period. Moreover, the agents
successfully flatten the epidemic curve. This study creates potential to
improve dynamic system modeling by offering a way to represent human brain,
reasoning, and decision making. | http://arxiv.org/pdf/2307.04986 | Ross Williams, Niyousha Hosseinichimeh, Aritra Majumdar, Navid Ghaffarzadegan | cs.AI, cs.MA, econ.GN, nlin.AO, physics.soc-ph, q-fin.EC | null | null | cs.AI | 20230711 | 20230711 | [] |
2307.05300 | 55 | Hugo Touvron, Louis Martin, Kevin Stone, Peter Al- bert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023. Llama 2: Open founda- tion and fine-tuned chat models. arXiv preprint arXiv:2307.09288.
Zhenhailong Wang, Xiaoman Pan, Dian Yu, Dong Yu, Jianshu Chen, and Heng Ji. 2022. Zemi: Learn- ing zero-shot semi-parametric language models from multiple tasks. arXiv preprint arXiv:2210.00185.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023. Chain-of-thought prompting elic- its reasoning in large language models.
Benfeng Xu, An Yang, Junyang Lin, Quan Wang, Chang Zhou, Yongdong Zhang, and Zhendong Mao. 2023. Expertprompting: Instructing large language models to be distinguished experts. arXiv preprint arXiv:2305.14688. | 2307.05300#55 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 56 | Importance Sampling in PPO aims to rectify the policy divergence between the historical generative model and current model when optimizing policy model with responses in the experience buffer. EasyRL [37] argues that an oversized buffer would induce a wrong estimation of the advantage of the current policy, which impairs the stability of the policy optimization. We revalidated this hypothesis by directly ï¬xing the policy distribution to observations of reference model, which is equivalent to having an inï¬nite experience buffer in the training process. We ï¬nd this setup doesnât have as severe impacts as expected, and only exhibits ï¬uctuations in the later stage of training. We additionally investigate the cooperative effect of this setup with KL penalties in view that they share similar controls on PPO. Experimental results indicate that this implementation further stabilizes PPO training, but compromises the ï¬nal performance of the policy model. | 2307.04964#56 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 56 | Tianci Xue, Ziqi Wang, Zhenhailong Wang, Chi Han, Pengfei Yu, and Heng Ji. 2023. Rcot: Detect- ing and rectifying factual inconsistency in reason- ing by reversing chain-of-thought. arXiv preprint arXiv:2305.11499.
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601.
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022. React: Synergizing reasoning and acting in language models. ArXiv, abs/2210.03629.
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022. Automatic chain of thought prompting in large language models. | 2307.05300#56 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 57 | Entropy Bonus provides a reference model-independent constraint on PPO training. There is controversy in past research about whether this method is effective in different scenarios. Mnih [36] reported that entropy bonus could enhance exploration by encouraging policy models to generate more diverse actions, while others did not ï¬nd clear evidence that such operations help [28]. We claim that these views can coexist as conï¬gurations regarding entropy bonus exhibit vast sensitivity on parameter selection and code implementation. A comparison of successful and failed experiments is presented in appendix B.3. With correct conï¬gurations, we did not ï¬nd an obvious advantage of this trick relative to KL-penalty. We, therefore, recommend the latter instead of directly constraining the diversity of the strategy space.
# 5.3.3 Pretrained Initialization | 2307.04964#57 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 57 | Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022. Automatic chain of thought prompting in large language models.
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena.
# A Prompts
# A.1 SPP Prompt Design
To prompt an LLM to behave as a cognitive syner- gist that follows the expected task-solving proce- dure as mentioned in §2, we carefully designed the structure of the SPP prompt as follows. The full prompts can be found in § A.2.3
System Principle. The first part of the prompt contains a high-level instruction: "When faced with a task, begin by identifying the participants who will contribute to solving the task. Then, initiate a multi-turn collaboration process until a final solution is reached. The participants will give critical comments and detailed suggestions whenever necessary." | 2307.05300#57 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 58 | # 5.3.3 Pretrained Initialization
A common setting is to initialize the policy and critic model over the existing reference model and reward model in RLHF. Such initialization is quite rare in past research scenarios and its impact on PPO training is still unexplored. We investigated different initialization methods at the early stage of training, expecting to uncover the requirements of RLHF for the trained model capabilities. The training discrepancy induced by different initialization methods is shown in Figure 8. The initialization of the critic model did not signiï¬cantly affect the convergence or ï¬uctuation of the PPO and only varied the numerical stability at the early stage of optimization. In contrast, a policy model initialized without SFT training is clearly incapable in PPO training, which indicates that the construction of a supervised policy model is indispensable in RLHF.
Critic Model Initialization We ï¬rst discuss the inï¬uence of different critic model initialization on PPO training. An observation is that the critic model requires giving feedback to each step in the decision sequence, and introduces a gap between this task requirement and directly scoring response, which makes it a less-than-perfect choice to initialize the critic model with the reward model. We explore this issue by applying a different initialization. Considering that providing correct score feedback for a single action requires the model to have basic language modeling capability, we design two scenarios to vary the consistency between the critic model initialization and its training
15 | 2307.04964#58 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 58 | Demonstration Examples. Then, we include two manually crafted demonstration examples to showcase the expected task-solving behavior. The first example describes a Game of 24 task, where we only include two personas: an AI Assistant and a Math Expert. This task aims to provide an ex- ample of a reasoning-intensive task, where the AI Assistant needs to propose multiple proposals, and the other participants need to give fine-grained feed- back on where the current solution went wrong and how to improve it. The second example describes a poem-writing task with diverse requirements, in- cluding lexical constraints, semantic constraints, and audience awareness. This task aims to provide an example of a knowledge-intensive task, where di- verse personas are required to collaboratively solve the task. This example also demonstrates a case where it is important to assign a dedicated persona to the audience, e.g., a ten-year-old child.
Task Prefix. The last part of the prompt reminds the model "identify the participants and collaboratively solve fol- the following task step by step." lowed by task-specific format instructions and inputs.
# A.2 Full Prompts
Figures 15, 16 and 17 show the full prompts for SPP, SPP-Profile and SPP-Fixed-Persona respec- tively. Figure 18 shows the prompts for Chain-of3We use the same prompt for any arbitrary tasks. | 2307.05300#58 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 59 | 15
300 04 8 4 spare ioe Ory 525 03 ; 2 2 g 200 Fa 5 Po2 Zo 2197 Nach 3 â Es 8 100 a 7 3 goa 2 2 « 50 -4 ° 0.0 5 o 260 4606008001000 o 20 4606008001000 ¢ 200 400 600 800 1000 Step Step Step 10 120 to 2s > 80 20 F g 054!) 8 3x05 s h g15 é 40 2 0.0] ~ | \iicninntibenrnncsantnn $10 ~ ae |, os a 5 0 a 3.00 0 200 400 600 800 1000 0 200 400 600 800 1000 0 200 400 600 800 1000 Step Step Step 4 â pPo-Max â Init Policy Model=Pretrained Model ° 24 |) ââ Init Critic Model=SFT Model & - Ih Pretrain Critic Model before PPO <⬠By pk & 2 , : #1 -4 o 260 460600800 1000 o 260 4006008001000 Step Step | 2307.04964#59 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 59 | Impact of the Demonstrations in SPP Prompt 82 : | | 70 66 = 60 s 2 © 40 oO wn 20 @m original la w/o multi-persona demo 0 en eee Trivia Creative | Codenames Logic Grid Writing (N=5) Collaborative Puzzle
Figure 9: Analysis on the impact of the demonstration examples in SPP prompt. We compare the effectiveness of the original SPP prompt with a variant where we re- move the second demonstration example, which shows a multi-persona scenario. We observe that (1) SPP is fairly robust to the change in the prompt; (2) adding an additional multi-persona example apart from the single- persona one effectively boosts performance on all three tasks.
Thought (CoT) prompting. Figure 19 shows the prompts for Self-Refine prompting.
# B Task Details
# B.1 Trivia Creative Writing | 2307.05300#59 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 60 | Figure 8: We show the necessity regarding supervised ï¬ne-tuning (SFT) of the policy model and the non-necessity regarding speciï¬c initialization of critic model. In the subï¬gure about KL-divergence and perplexity, the right axis represents the result under initiating policy model without SFT. Itâs a reduction to RLHF process when initializing the critic model with SFT model or omitting the ï¬ne-tuning process on policy model, we experiment with these changes on the basis of PPO-max. Pre-training the critic model introduced additional processing to PPO and provides more stable optimization.
objective: (1) Initialize the critic model with our SFT model and randomly initialize its reward head. (2) Optimize only the reward model until the loss of value prediction function approaches zero. We show the training dynamics of this setup starting from the optimization policy model in Figure 8. | 2307.04964#60 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 60 | Thought (CoT) prompting. Figure 19 shows the prompts for Self-Refine prompting.
# B Task Details
# B.1 Trivia Creative Writing
Figure 3 shows a detailed illustration of the Trivia Creative Writing task. Additionally, we investi- gate how the number of the questions (N) and the ordering of the questions would affect the perfor- mance on the Trivia Creative Writing task. As shown in Figure 10, with a larger number of ques- tions (Nâ¥5), Trivia Creative Writing effectively challenges GPT-4âs performance. While a single question (N=1) yields similar outcomes regardless of the prompting method, SPP approach is notably superior for larger Ns. The ordering of the ques- tions has minimal impact to the task performance. The topic list is automatically generated by prompting GPT-4 to provide 100 nouns from pop culture4.
# C Inference Configurations
The main results in Table 2 are obtained from GPT- 4. The GPT-4 API version we employ is Azure 2023-3-15-preview.5 The temperature is set to 0.0
4The full prompt for generating the topic list can be found in Figure 20. We performed further human curation to avoid potential harmful content.
5There are rare cases when a generation triggers the content filter of the API. We exclude those instances from our results. | 2307.05300#60 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 61 | Based on the experimental results, we believe the critic model pre-training helps to improve the training stability by providing better advantage estimation. Initializing the critic model with a reward or SFT model will converge to similar results, implying that PPO can adaptively provide the capability to ï¬t the advantage function. Intuitively, ï¬uctuations in the early training period imply that the model is focusing on optimizing the critic model and does not have a consistent optimization direction in terms of generation policies. We recommend replacing the learning rate warmup with the critic model pre-training as a generic initialization strategy.
Policy Model Initialization An interesting question is whether we need to supervise ï¬ne-tuning our pre-train model before PPO, we wondered about the feasibility of directly enabling language models to interact with humans through policy optimization. Unfortunately, such attempts failed and we observed a severe reduction in language modeling ability in the training results, which implies that a qualiï¬ed dialogue model is essential for underlying PPO training. Furthermore, we notice that the train model response obtains lower rewards relative to the policy model after SFT, which may provide circumstantial evidence for the effectiveness of using human preference data to directly ï¬ne-tune the model for alignment.
# 5.4 PPO-max Setup | 2307.04964#61 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 61 | 5There are rare cases when a generation triggers the content filter of the API. We exclude those instances from our results.
The Impact of N on Trivia Creative Writing 8° os =~ 60 x § 40 g â@- Standard â@- CoT 20] -@- Self Refine â@- SPP () 1 Trivia Creative Trivia Creative Trivia Creative Writing N=1 Writing N=5 ~â Writing N=10
The Impact of Question Ordering on Trivia Creative Writing 7 es g 60 g § 407 -@- Standard â â@- CoT 2071 -®- Self-Refine â@- SPP 0 T T Trivia Creative Trivia Creative Writing N=5 Writing N=5 Shuffled
(a) Trivia Creative Writing with a large enough number of questions (N) effectively pose challenge to GPT-4 in terms of factual correctness. With N=1, different prompting methods result in similar performance, while with N>=5, SPP shows visible superiority.
(b) The ordering of the questions in the Trivia Creative Writing task does not bring too much impact. The per- formance on shuffled questions is close to the original ordered questions.
Figure 10: Analysis on the impact of the number of questions (N) and the ordering of the questions for the Trivia Creative Writing task. | 2307.05300#61 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 62 | # 5.4 PPO-max Setup
We now describe our training implementations in the PPO-max algorithm. Based on the discussion and validation in Sec 5.3, we selected the most effective strategy for each component of PPO. We normalize and clip the current group of rewards based on historical mean and variance records, and subsequently add a KL-penalty term to constrain the policy optimization. In the model loading phase,
16
we initialize the critic model with our reward model and pre-train it before applying PPO formally. We use global gradient clipping and set a small size of the experience buffer. To reduce alignment tax, we add pre-train language model loss in policy optimization as InstructGPT [16] and simultaneously clip the value function loss. More detailed settings can be found in our open-source code. We show the complete training dynamics of PPO-max in Figure 9. | 2307.04964#62 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 62 | Figure 10: Analysis on the impact of the number of questions (N) and the ordering of the questions for the Trivia Creative Writing task.
(most conservative) and top_p to 1.0 for all gen- erations to maximize reproducibility. Since even though the temperature is set to 0.0 the GPT-4 gen- eration can still be non-deterministic, we conduct additional experiment to investigate its generation consistency under this configuration. As shown in Table 3, we perform three individual runs and com- pute the mean and standard deviation of the metric score on Trivia Creative Writing. We find that the variance is sufficiently small and Solo Performance Prompting enjoys lower variance than Standard and CoT prompting.
Methods Run 1 Run 2 Run 3 Mean (std) Standard CoT SPP 75.6 68.8 80.0 74.4 69.6 79.8 73.1 70.8 80.8 74.4 ±1.3 69.7 ±1.0 80.2 ±0.5 | 2307.05300#62 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 63 | ââ PPO-Max 280 25 260 240 2.0 = 220 Pp 4 Bis 200 5 2 g 5 180 10 © 160 140 os 120 0 2000 4000 6000 8000 10000 te) 2000 4000 6000 8000 10000 Step Step 1.030 0.07 1.025 0.06 © 0.05 2.020 2 > $0.04 3 4 1.015 a 0.03 & g 1.010 0.02 0.01 1,005, 0.00 1.000 0 2000 4000-6000. ~-a000 10000 i) 2000 4000-6000 8000 10000 Step Step
Figure 9: 10K steps training dynamics of PPO-max. PPO-max ensures long-term stable policy optimization for the model.
# 6 Evaluations and Discussions
In this section, we provide a detailed analysis of the advantages of the RLHF models over the SFT models. These advantages are evident not only in the direct comparison between RLHF and SFT models but also in their performance gap when facing ChatGPT.
# 6.1 Alignment Metrics and Experiment Setups
Alignment is a vague and confusing topic that is intractable to evaluate. In the context of our paper, we endeavor to align models with human intentions. To be more speciï¬c, we deï¬ne models to act as being helpful and harmless similar to [27]. | 2307.04964#63 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 63 | Table 3: Investigation on the generation consistency of GPT-4 API. The experiment is performed on the Trivia Creative Task (N=5). We set the inference temperature to 0.0 and top_p to 1.0 as all experiments conducted in the paper. The results show that the GPT-4 generation is fairly consistent with a small variance (â¼ 1%). We also observe that SPP shows lower variance compared with Standard and CoT prompting across different runs.
sage. We report the average metric scores across both inference settings in Table 2. Full GPT-4 re- sults for each setting can be found in Appendix F. For GPT-3.5 results in Figure 6, we employ the same prompt, hyper-parameters and the best system message setting in terms of SPPâs GPT-4 perfor- mance. For Llama2, we leverage the Huggingface text-generation pipeline6 with greedy decoding.
# D Additional Qualitative Analysis
Figure 11 presents examples of the Trivia Creative Writing task, illustrating that although CoT can generate plausible plans for task resolution, the fi- nal outcomes often contain factual inaccuracies and instances of hallucination. In contrast, SPP elicits precise knowledge with fine-grained personas. | 2307.05300#63 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 64 | Helpfulness means the model should follow instructions; it must not only follow instructions but also deduce the intent from a few-shot prompt or another interpretable pattern. However, the intention behind a given prompt can often be unclear or ambiguous, which is why we depend on our annotatorsâ judgment, and their preference ratings constitute our primary metric.
Harmlessness is also challenging to measure. The extent of damage caused by language models usually depends on how their outputs are utilized in the real world. For instance, a model that generates toxic outputs could be harmful in a deployed chatbot but could also be beneï¬cial if used for data augmentation to train a more precise toxicity detection model.
As a result, we employ more precise proxy criteria to capture various aspects of a deployed modelâs behavior that can be helpful or harmful. In order to compare the RLHF models with baseline models, we generate a single response for each test prompt and task human annotators by comparing the responses from different models and labeling their preferences. We repeat this experiment multiple times using GPT-4 as the annotator and consistently obtain agreement levels between the evaluations.
17
(a) Human preference evaluation. (b) Evaluation using GPT-4 as the annotator.
Figure 10: Preference evaluations, compared RLHF models with SFT models in human evaluation (left) and GPT-4 evaluation (right). | 2307.04964#64 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 64 | Figure 12 displays examples of the Codenames Collaborative task, illustrating that SPP generates intermediate dialogues that are both detailed and interpretable, leading to superior performance com- pared to CoT.
Figure 13 shows additional qualitative examples on Solo Performance Prompting vs SPP-Profile.
# E Early-termination with SPP-Fixed-Persona
To evaluate the potential impact of initial persona assignment through a system message, we consider two inference settings: with or without the default system message, "You are an AI assistant that helps people find information". Di- vergent patterns are observed across various tasks and methods regarding the use of the system mesFigure 14 shows an example of the early- termination problem (defined in § 4) where the generation stops before reaching the final solution as if the models is waiting input from an external user.
6https://huggingface.co/blog/llama2 | 2307.05300#64 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 65 | Figure 10: Preference evaluations, compared RLHF models with SFT models in human evaluation (left) and GPT-4 evaluation (right).
Baseline. We employ several baselines for comparison, including two SFT models trained on LLaMA and OpenChineseLLaMA datasets. These SFT models are trained on Chinese and English datasets, respectively. Additionally, we derive two RLHF models using PPO-max from these two types of SFT models 3 We also compare our models with OpenAIâs ChatGPT 4 (gpt-3.5-turbo-0613), an excellent language model tuned with RLHF.
Generation. We generate a single response for each prompt using nucleus sampling [30] with a probability threshold of p = 0.9 and a temperature of Ï = 0.8 for each baseline model. To avoid repetitive responses, we apply a repetition penalty [38] with a hyperparameter of β = 1.1 based on previously generated tokens. Additionally, we set the maximum token length to 2048.
# 6.2 Preference Comparison between RLHF models and SFT models | 2307.04964#65 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 65 | 6https://huggingface.co/blog/llama2
SPP v.s. CoT (Trivia Creative Writing N=5) (â >) Instance Index: 10 Question #5: In which country is the Sky Train Rail bridge? iq SPP D} C CoT ») Participants: Al Assistant (you); Movie Expert; Art Expert; Plan: Sports Expert; Geography Expert [.] LJ 6. Incorporate the Sky Train Rail bridge, which is in Geography Expert: The Sky Train Rail Bridge is located Thailand. < (factual error) in Canada. Instance Index: 12 Question #5: Which 70s show was based on the British show Till Death Us Do Part? SPP ) C CoT Participants: Al Assistant (you); Story Writer; Music Plan: Expert; TV Show Expert; Cultural Expert [...] 6. Mention a show that the Little Mermaid watches, LJ based on the British show Till Death Us Do Part. TV Show Expert: [...] The 70s show All in the Family Story: was based on the British show Till Death Us Do Part. [...] Ariel and her friends often gathered to watch their favorite show, "Undersea Squabbles," (hallucination) which was based on the British show Till Death Us Do Part. X / | 2307.05300#65 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 66 | # 6.2 Preference Comparison between RLHF models and SFT models
Human evaluation is known to be both time-consuming and costly, yet it remains crucial for obtaining human-aligned assessments and serving as a reliable foundation for comprehensive evaluation. Following a similar approach as InstructGPT [16], our primary metric for evaluation is based on human preference ratings derived from a held-out set of prompts. It is important to note that we only select prompts that have not been included in the training process, ensuring unbiased evaluation.
Furthermore, incorporating the expertise of GPT-4, the most powerful model to date, to compare responses from different chatbots offers valuable insights and enhances the evaluation process. This approach aligns with the ï¬ndings of studies such as AlpacaFarm [39] and LLM-as-a-judge [40], which suggest that end-to-end automation evaluation can provide a relatively fair assessment when compared to human preferences. Therefore, in this paper, we follow a similar evaluation method in LLM-as-a-judge [40] and supplement the overall evaluation process with GPT-4. | 2307.04964#66 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 66 | Figure 11: SPP vs CoT qualitative examples on Trivia Creative Writing (N=5). We find that although CoT generates reasonable plans or steps, it tends to suffer from factual errors and hallucination.
The problem is particularly severe on certain tasks, e.g., Codenames Collaborative, resulting in unexpectedly low performance as shown in Fig- ure 7b. The problem can be largely alleviated by removing the system message but cannot be en- tirely eliminated. Table 4 shows the statistics of the early-termination problem for each task and method. In contrast, we did not observe early- termination on SPP, SPP-Profile, Standard, or CoT prompting with GPT-4.
# F Full Results
Full results of the three tasks: Trivia Creative Writ- ing, Codenames Collaborative and Logic Grid Puz- zle can be found in Tables 5, 6 and 7, respectively.
# G Usage of AI assistants in writing
We used ChatGPT and GPT-4 solely for checking and correcting grammars.
Tasks added system message Trivia Creative Writing (N=5) yes no 18 / 100 0 / 100 Trivia Creative Writing (N=10) yes no 16 / 100 1 / 100 Codenames Collaborative yes no 37 / 50 4 / 50 Logic Grid Puzzle yes no 11 / 200 15 / 200 | 2307.05300#66 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 67 | Human Evaluation. Our annotators consistently expressed a strong preference for the outputs of RLHF-trained models across all question types in both Chinese and English, as illustrated in Figure 10. Speciï¬cally, the RLHF model on the English dataset exhibits signiï¬cant advantages on the Harmless held-out dataset, receiving a rating of 62% compared to 5% for the SFT model. These ï¬ndings indicate that the RLHF model substantially enhances its ability to address a wide range of issues, including personal privacy, political sensitivity, and the handling of toxic and biased prompts within minority communities and ethnic groups. Additionally, there is a slight improvement observed in the Helpful held-out dataset, with a rating of 44% compared to 30% for the SFT model, suggesting that the SFT model can also beneï¬t from optimization via RLHF. We have also demonstrated that our RLHF model enhances the performance of the SFT model on both the Helpful and Harmless datasets in the Chinese domain. This showcases the substantial potential of PPO-max in the RLHF phrase.
3We differentiate between two language models, one trained on English text (âenâ) and the other on Chinese text (âzhâ).
# 4https://platform.openai.com/docs/models
18 | 2307.04964#67 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 67 | Table 4: Early termination statistics on SPP-Fixed-Persona: Removing the system message, "You are an AI assistant that helps people find information.", can effectively reduce the problem but cannot fully eliminate it.
Methods Scores (N = 5) (%) w/ system message w/o system message average max Standard CoT 75.6 68.8 73.6 65.6 74.6 67.1 75.6 68.8 Self-Refine [iter=0] Self-Refine [iter=1] 74.9 75.3 72.7 72.5 73.8 73.9 74.9 75.3 SPP-Fixed-Persona SPP-Profile SPP 66.1 79.8 80.0 79.6 78.3 79.8 72.9 79.1 79.9 79.6 79.8 80.0 | 2307.05300#67 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 68 | # 4https://platform.openai.com/docs/models
18
mmm Ours Win «mmm Tie Ours Lose RLHF (en) %e 24% 45% RLHF (zh) (3 29% SFT (zh) $89 37% 0% 20% 40% 60% 80% 100%
Figure 11: Preference comparison on the âharmlessâ evaluation between our RLHF and SFT models versus ChatGPT (gpt-3.5-turbo-0613) reveals that the RLHF-trained models exhibit a signiï¬cant reduction in the number of queries being outperformed by ChatGPT.
GPT-4 as a Judge. While GPT-4 may not be a perfect evaluator, we can observe some similarities between its results and human evaluations. In our GPT-4 evaluation setting, the results closely mirror those of human evaluation, as depicted in the right sub-ï¬gure of Figure 10. When assessing harmful prompts, the RLHF model trained on the English dataset continues to demonstrate signiï¬cant advantages in the Harmless dataset, despite GPT-4 producing more tie votes than human evaluators. This trend is also apparent in the Chinese Harmless evaluation. Notably, Figure 10 highlights a substantial improvement in the RLHF model, particularly in helpful datasets, compared to evaluations based on human preferences. | 2307.04964#68 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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}
] |
2307.05300 | 68 | Methods Scores (N = 10) (%) w/ system message w/o system message average max Standard CoT 77.2 71.6 76.8 65.3 77.0 68.5 77.2 71.6 Self-Refine [iter=0] Self-Refine [iter=1] 77.1 78.2 75.4 75.6 76.3 76.9 77.1 78.2 SPP-Fixed-Persona SPP-Profile SPP 70.5 82.3 85.2 81.3 83.8 84.2 75.9 83.0 84.7 81.3 83.8 85.2
Table 5: Trivia Creative Writing full results, including two inference settings: with system message and without system message. "average" and "max" indicating the mean and max score across the two settings. The system message we use is: âYou are an AI assistant that helps people find information.â
# SPP v.s. CoT (Codenames Collaborative)
# la
# Instance Index: 9
Word List: shoulder, glacier, anchor, magician, tunnel, sugar, gear, book, rainbow, ram, nose, pacific, volume, rope, mark, drum, page, sack, kick | 2307.05300#68 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 69 | # 6.3 Our Models vs. ChatGPT on Harmless Evaluation
In this part, we conduct a comparison between our model and one of the most popular existing models, ChatGPT. Our objective was to showcase the advantages of the RLHF model when facing a more formidable opponent, rather than aiming to surpass ChatGPT. To achieve this, we select the âharmlessâ capability as our comparative metric, and we employ GPT-4 for automated evaluations.
Mitigating Defeats to ChatGPT. Figure 11 provides evidence that our RLHF models still lag behind OpenAIâs ChatGPT. However, we observe signiï¬cant improvements in our RLHF models compared to the SFT models, particularly in mitigating losses when facing ChatGPT. Speciï¬cally, the RLHF model trained on English text managed to decrease the defeat rate from 45% to 24%. Similarly, the RLHF model trained on Chinese text achieved a reduction in the defeat rate from 37% to 29%. While surpassing ChatGPTâs performance remains a challenging task, it is noteworthy that the RLHF models were able to compete on par with ChatGPT on certain prompts where the SFT models previously failed. This indicates that the RLHF approach enhances the modelsâ ability to generate more effective responses and bridge the gap between their performance and that of ChatGPT.
# 6.4 Language Understanding Evaluation | 2307.04964#69 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 69 | page, Target Words: kick, rope ( SPP Spymaster > Participants: Al Assistant (you); Language Expert; Word Association Expert Language Expert: The task is to find a single word hint that can accurately represent and link the 2 given words: "kick, rope". The hint should not cause confusion with other words from the list provided. Word Association Expert: Let's first analyze the words "kick" and "rope". We can think of activities or objects that involve both kicking and using a rope. For example, sports or games. Al Assistant (you): Thanks for the suggestions! Based on the idea of sports or games, | propose the hint "jump". It can be associated with both "kick" and "rope" without causing confusion with the other words in the list. [.] Final answer: jump â4 1 Va CoT Spymaster Steps: 1. Analyze the given words: "kick" and "rope". 2. Identify the common theme or connection between the two words. 3. Compare the connection with the list of words provided to ensure the hint does not cause confusion with other words. 4. Select a hint that accurately represents and links the two given words without causing confusion. Answer: Sport â, XX v v SPP Spymaster | 2307.05300#69 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 70 | # 6.4 Language Understanding Evaluation
To examine the potential decline in Natural language understanding (NLU) abilities resulting from ï¬netuning models using PPO, we conduct tests on Chinese RLHF model using the C-Eval5, which is a comprehensive Chinese evaluation suite for foundation models. It consists of approximately 13k multi-choice questions spanning 52 diverse disciplines and four difï¬culty levels. We primarily evaluate our models in the initial release, whose results are from few-shot prompting.
The experimental results indicate a decrease in NLU capabilities after employing PPO. By incor- porating pre-training data into the PPO training phase, PPO-ptx effectively alleviates the decline in NLU capabilities. The rationale behind this method was to leverage the knowledge acquired during pre-training and combine it with the reinforcement learning framework of PPO.
# 5https://github.com/SJTU-LIT/ceval
19
40 =~ PPO-max lm ~PPO-ptx 30 ig § 20 wn 10 i?) STEM Social Sciences Humanities Others Average Categories
Figure 12: The bar chart displays the results of C-eval for SFT, PPO-max, and PPO-ptx respectively. The result demonstrates that PPO-ptx mitigates the decline in language understanding capabilities caused by PPO.
# 6.5 Example Dialogues | 2307.04964#70 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 70 | other words. 4. Select a hint that accurately represents and links the two given words without causing confusion. Answer: Sport â, XX v v SPP Spymaster Hint: jump CoT Spymaster Hint: Sport v v ( SPP Guesser ) 7 ( CoT Guesser +) \ Participants: Al Assistant (you); Linguist; Athlete Steps: Linguist: When thinking about words associated with "jump," consider words that are related to physical activities, sports, or movement. Look for words that can be used in the same context as "jump" or have a similar meaning. Athlete: In sports and physical activities, we often use equipment or tools that help us jump or perform jumping- related actions. Keep that in mind when selecting the words. Al Assistant (you): Thanks for the guidance! Based on your suggestions, | think "rope" and "kick" are the two words best associated with "jump" from the list. Rope can be related to jumping rope, and kick can be associated with jumping in some sports like soccer or martial arts. Linguist: Let's verify if the words are related to "jump." Rope is indeed related to jumping rope, which is a common physical activity. Kick is also related to jumping in some sports, | 2307.05300#70 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 71 | # 6.5 Example Dialogues
To provide a more intuitive demonstration of our modelâs dialogue abilities, we present some dialogue examples in Tables 2 and 3. It is evident that the RLHF-trained model generates responses with a higher level of informational content compared to the SFT model. These responses effectively assist in addressing user prompts. Moreover, the SFT model demonstrates a basic ability to identify harmful prompts, but it still remains susceptible to producing harmful outputs when prompted accordingly. In contrast, the RLHF model exhibits superior judgment when it comes to harmful content and is less prone to inducements, displaying a higher degree of coherency. More dialogue examples are presented in the appendix C.4.
# Limitations
Exploring RLHF is indeed a valuable but lonely direction, and we are glad that the core backbone of the laboratory can ï¬rmly explore an uncertain direction. Moreover, in the past few months, everyone has been so full of passion and motivation. RLHF not only allows the models to achieve human alignment, but also seems to align everyoneâs will.
A thousand mile journey begins with the ï¬rst step. Although we have taken the ï¬rst step in RLHF, due to time and resource constraints, this work still has the following limitations: | 2307.04964#71 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 71 | if the words are related to "jump." Rope is indeed related to jumping rope, which is a common physical activity. Kick is also related to jumping in some sports, as you mentioned. Both words seem to be good choices. Athlete: | agree with the choices. Rope and kick are both related to jumping and physical activities. Final answer: rope, kick 1. Review the list of words provided. 2. Identify words that are directly related to or commonly associated with sports. 3. Select the top 2 words that are most closely related to sports. Answer: gear, kick Ne Score: 2/2=1.0 Score: 1/2=0.5 | 2307.05300#71 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 72 | Scaling Law. While our study primarily focuses on a 7-billion-parameter model, we have yet to investigate the impact of model size and data scale on the performance of RLHF.
Reward Model. Our experiments are based on openly available English human preference datasets and a small amount of self-constructed Chinese data. The quality and quantity of the data we have at our disposal are arguably not sufï¬cient for a comprehensive evaluation of the reward model.
Evaluation Metric. Our evaluation criteria largely rely on manual evaluations and GPT-4 auto- mated evaluations. We have not utilized numerous available benchmarks and NLP tasks to conduct a detailed assessment of our models.
Performance Indicator. Our focus during the PPO phase is more geared towards achieving stability rather than enhancing the ï¬nal performance. While stability is crucial, it does not necessarily guarantee improved outcomes. Additionally, the reward score cannot reliably serve as an indicator for predicting RLHF performance during the training phase. It implies that a more suitable performance indicator during the training phase needs to be sought.
20
# References
[1] Touvron, H., T. Lavril, G. Izacard, et al. Llama: Open and efï¬cient foundation language models. arXiv preprint arXiv:2302.13971, 2023. | 2307.04964#72 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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"id": "2302.13971"
},
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"id": "1909.08593"
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"id": "2201.08239"
},
{
"id": "2304.03277"
},
{
"id": "2304.03442"
},
{
"id": "2303.18223"
},
{
"id": "2108.07258"
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{
"id": "2204.05862"
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{
"id": "2303.03378"
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"id": "2203.02155"
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{
"id": "2112.00861"
},
{
"id": "2201.11903"
},
{
"id": "1904.09751"
}
] |
2307.04964 | 73 | [2] Chiang, W.-L., Z. Li, Z. Lin, et al. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. See https://vicuna. lmsys. org (accessed 14 April 2023), 2023.
[3] OpenAI. Gpt-4 technical report, 2023.
[4] Zhao, W. X., K. Zhou, J. Li, et al. A survey of large language models. arXiv preprint arXiv:2303.18223, 2023.
[5] Brown, T., B. Mann, N. Ryder, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877â1901, 2020.
[6] Peng, B., C. Li, P. He, et al. Instruction tuning with gpt-4. arXiv preprint arXiv:2304.03277, 2023.
[7] Taori, R., I. Gulrajani, T. Zhang, et al. Stanford alpaca: An instruction-following LLaMA model. https://github.com/tatsu-lab/stanford_alpaca, 2023. | 2307.04964#73 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 73 | SPP v.s. SPP-Fixed-Persona (Trivia Creative Writing N=5) Instance Index: 30 Question #1: Which orchestral conductor was married to one of the subjects of the film Hillary and Jackie? ( SPP > C SPP-Fixed-Persona p Participants: Al Assistant (you); Film Expert; Literary Participants: Al Assistant (you); Expert Expert; History Expert; Geography Expert; Architecture Expert: Sir Simon Rattle was married to one of the Expert subjects of the film Hillary and Jackie. Film Expert: The orchestral conductor married to one of the subjects of the film Hillary and Jackie is Daniel Barenboim. Instance Index: 7 Question #3: Which musician founded the Red Hot Peppers? C SPP ( SPP-Fixed-Persona >) Participants: Al Assistant (you); Storyteller; Mythology Participants: Al Assistant (you); Expert Expert; Music Enthusiast; Historian; Speed Record Expert Al Assistant (you): The musician who founded the Red Music Enthusiast: The musician who founded the Red Hot Peppers is Louis Armstrong. Hot Peppers is Jelly Roll Morton. Instance Index: 56 Question #1: What sport or pastime would you be playing if your balls were black and blue | 2307.05300#73 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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{
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{
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] |
2307.04964 | 74 | [8] Wei, J., X. Wang, D. Schuurmans, et al. Chain of thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201.11903, 2022.
[9] Driess, D., F. Xia, M. S. Sajjadi, et al. Palm-e: An embodied multimodal language model. arXiv preprint arXiv:2303.03378, 2023.
[10] Park, J. S., J. C. OâBrien, C. J. Cai, et al. Generative agents: Interactive simulacra of human behavior. arXiv preprint arXiv:2304.03442, 2023.
[11] Lucy, L., D. Bamman. Gender and representation bias in gpt-3 generated stories. In Proceedings of the Third Workshop on Narrative Understanding, pages 48â55. 2021.
[12] Thoppilan, R., D. De Freitas, J. Hall, et al. Lamda: Language models for dialog applications. arXiv preprint arXiv:2201.08239, 2022. | 2307.04964#74 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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"id": "2201.08239"
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2307.05300 | 74 | Louis Armstrong. Hot Peppers is Jelly Roll Morton. Instance Index: 56 Question #1: What sport or pastime would you be playing if your balls were black and blue and your opponent's were red and yellow? C SPP ( SPP-Fixed-Persona > Participants: Al Assistant (you); Story Writer; Sports Participants: Al Assistant (you); Expert Enthusiast; Historian; Movie Buff; Music Expert; Mythology Expert: Snooker 9¢ Expert Sports Enthusiast: The sport with black and blue balls for one player and red and yellow balls for the other is Croquet. \ yy, | 2307.05300#74 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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"id": "2302.04023"
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] |
2307.04964 | 75 | [13] Bender, E. M., T. Gebru, A. McMillan-Major, et al. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 610â623. 2021.
[14] Bommasani, R., D. A. Hudson, E. Adeli, et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021.
[15] Altman, S. Planning for agi and beyond. https://openai.com/blog/planning-for-agi-and-beyond, 2022.
[16] Ouyang, L., J. Wu, X. Jiang, et al. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155, 2022.
[17] Bai, Y., A. Jones, K. Ndousse, et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862, 2022. | 2307.04964#75 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 75 | Question #1: Which orchestral conductor was married to one of the subjects of the film Hillary and Jackie? ( SPP > C SPP-Fixed-Persona p Participants: Al Assistant (you); Film Expert; Literary Participants: Al Assistant (you); Expert Expert; History Expert; Geography Expert; Architecture Expert: Sir Simon Rattle was married to one of the Expert subjects of the film Hillary and Jackie. Film Expert: The orchestral conductor married to one of the subjects of the film Hillary and Jackie is Daniel Barenboim. Instance Index: 7
C SPP ( SPP-Fixed-Persona > Participants: Al Assistant (you); Story Writer; Sports Participants: Al Assistant (you); Expert Enthusiast; Historian; Movie Buff; Music Expert; Mythology Expert: Snooker 9¢ Expert Sports Enthusiast: The sport with black and blue balls for one player and red and yellow balls for the other is Croquet.
Figure 13: SPP vs SPP-Fixed-Persona qualitative examples on Trivia Creative Writing (N=5). Each example shows one of the trivia questions in the input instance, the identified participants and the provided answer. We observe that the dynamically identified fine-grained personas, such as "Film Expert", "Music Enthusiast" and "Sports Enthusiast", tend to outperform the fixed general personas, "Expert". | 2307.05300#75 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 76 | [18] OpenLMLab. Open-Chinese-LLaMA: Chinese large language model base generated through incremental pre-training on chinese datasets. https://github.com/OpenLMLab/ OpenChineseLLaMA, 2023.
[19] Chiang, W.-L., Z. Li, Z. Lin, et al. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, 2023.
[20] Ji, Y., Y. Deng, Y. Gong, et al. Belle: Be everyoneâs large language model engine. https: //github.com/LianjiaTech/BELLE, 2023.
[21] Beeching, E., Y. Belkada, K. Rasul, et al. StackLLaMA: An RL ï¬ne-tuned LLaMA model for stack exchange question and answering, 2023.
21
[22] Christiano, P. F., J. Leike, T. Brown, et al. Deep reinforcement learning from human preferences. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett, eds., Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., 2017. | 2307.04964#76 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 76 | Methods Scores (%) w/ system message w/o system message average max Standard CoT 74.5 71.4 76.3 74.0 75.4 72.7 76.3 74.0 Self-Refine [iter=0] Self-Refine [iter=1] 77.3 70.1 73.2 58.8 75.3 64.4 77.3 70.1 SPP-Fixed-Persona SPP-Profile SPP 10.1 80.4 82.5 66.0 72.9 75.5 38.1 76.7 79.0 66.0 80.4 82.5
Table 6: Codenames Collaborative full results, including two inference settings: with system message and without system message. "average" and "max" indicating the mean and max score across the two settings. The system message we use is: âYou are an AI assistant that helps people find information.â
# ( Input
# Early-termination example with SPP on Llama2-13b-chat
âââ
# ir [... SPP procedure instructions and demonstration examples ...]
Now, identify the participants and collaboratively solve the following task step by step. Remember to present your final solution with the prefix "Final answer:". | 2307.05300#76 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 77 | [23] MacGlashan, J., M. K. Ho, R. Loftin, et al. Interactive learning from policy-dependent human feedback. In D. Precup, Y. W. Teh, eds., Proceedings of the 34th International Conference on Machine Learning, vol. 70 of Proceedings of Machine Learning Research, pages 2285â2294. PMLR, 2017.
[24] Ziegler, D. M., N. Stiennon, J. Wu, et al. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593, 2019.
[25] Stiennon, N., L. Ouyang, J. Wu, et al. Learning to summarize with human feedback. Advances in Neural Information Processing Systems, 33:3008â3021, 2020.
[26] Bai, Y., S. Kadavath, S. Kundu, et al. Constitutional AI: Harmlessness from AI feedback, 2022.
[27] Askell, A., Y. Bai, A. Chen, et al. A general language assistant as a laboratory for alignment. arXiv preprint arXiv:2112.00861, 2021. | 2307.04964#77 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 77 | Now, identify the participants and collaboratively solve the following task step by step. Remember to present your final solution with the prefix "Final answer:".
Task: {Write a short and coherent story about Harry Potter that incorporates the answers to the following 5 questions: Who was the man behind The Chipmunks? Which Lloyd Webber musical premiered in the US on 10th December 1993? Who was the next British Prime Minister after Arthur Balfour? Who had a 70s No 1 hit with Kiss You All Over? What claimed the life of singer Kathleen Ferrier?}
# Generation
# r
Participants: Al Assistant (you); Harry Potter Expert; Music Expert; History Expert; Medical Expert Start collaboration!
Ln The generation stopped here without simulating the multi- turn dialogue following the demonstration examples
Further Observation: Llama2 cannot follow the procedure as demonstrated in the examples, even with additional | 2307.05300#77 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 78 | [28] Andrychowicz, M., A. Raichuk, P. Sta´nczyk, et al. What matters for on-policy deep actor-critic methods? a large-scale study. In International Conference on Learning Representations. 2021.
[29] Engstrom, L., A. Ilyas, S. Santurkar, et al. Implementation matters in deep policy gradients: A case study on ppo and trpo, 2020.
[30] Holtzman, A., J. Buys, L. Du, et al. The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751, 2019.
[31] Mnih, V., A. P. Badia, M. Mirza, et al. Asynchronous methods for deep reinforcement learning. In M. Balcan, K. Q. Weinberger, eds., Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, vol. 48 of JMLR Workshop and Conference Proceedings, pages 1928â1937. JMLR.org, 2016.
[32] Jaques, N., A. Ghandeharioun, J. H. Shen, et al. Way off-policy batch deep reinforcement learning of implicit human preferences in dialog. CoRR, abs/1907.00456, 2019. | 2307.04964#78 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 78 | prompting, such as: "Follow the same procedure as the example tasks shown above. Simulate the dialogue of the participants." This indicates that a strong instruction-following ability is a prerequisite for SPP to effectively elicit cognitive synergy abilities. Ne âââ_â_â_â_âââ{_ Early-termination example with SPP-Fixed-Persona on GPT-4 > Input [... SPP-Fixed-Persona Prompt ...] Now, identify the participants and collaboratively solve the following task step by step. Note that the participants can only be either Al Assistant (you) or Expert. Remember to provide the final solution with the following format "Final answer: (a list of words here).". Task: {Try to identify the 4 words best associated with the word "film" from the following list: locust, fever, street, sherwood, kiss, popcorn, craft, ant, crystal, tear, cowboy, government, pine, mountie, soap, genie, king arthur, sphinx, director, bride, razor, fog, whistle. Your answer should be a comma-separated list of words.} Generation (Participants: Al Assistant (you); Expert Al Assistant (you): Let's first analyze the | 2307.05300#78 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 79 | [33] Schulman, J., S. Levine, P. Abbeel, et al. Trust region policy optimization. In International conference on machine learning, pages 1889â1897. PMLR, 2015.
[34] Schulman, J., F. Wolski, P. Dhariwal, et al. Proximal policy optimization algorithms, 2017.
[35] Huang, S., R. F. J. Dossa, A. Rafï¬n, et al. The 37 implementation details of proximal policy optimization. The ICLR Blog Track 2023, 2022.
[36] Mnih, V., K. Kavukcuoglu, D. Silver, et al. Human-level control through deep reinforcement learning. nature, 518(7540):529â533, 2015.
[37] Qi Wang, J. J., Yiyuan Yang. Easy RL: Reinforcement Learning Tutorial. Posts and Telecom Press, Beijing, 2022.
[38] Keskar, N., B. McCann, L. Varshney, et al. Ctrl: A conditional transformer language model for controllable generation. arXiv: Computation and Language, 2019.
[39] Dubois, Y., X. Li, R. Taori, et al. Alpacafarm: A simulation framework for methods that learn from human feedback, 2023. | 2307.04964#79 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 79 | Your answer should be a comma-separated list of words.} Generation (Participants: Al Assistant (you); Expert Al Assistant (you): Let's first analyze the task. We need to find the 4 words that are most closely associated with the word "film" from the given list. We can start by identifying words that are directly related to films, such as those related to movie genres, settings, or elements commonly found in films. Expert: That's a good approach. You can also consider words that are related to the experience of watching a film, such as items that are commonly consumed during a movie or words that describe the atmosphere of a film. Make sure to pick the 4 words that have the strongest association with "film". The generation stopped here without providing the Final answer | 2307.05300#79 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 81 | Human Prompt â How did life originate? (Helpful) SFT Response â There are several theories about how life on Earth originated, but none of them have been fully proven. Some scientists believe that life began in the form of simple organic molecules that formed from chemical reactions between simple compounds such as water, methane, and ammonia. These molecules may have formed on the surface of early Earth or in space and then came to Earth on a meteorite or comet. Other scientists believe that life arose from self-replicating molecules that formed spontaneously under conditions similar to those found deep within the Earthâs crust. Still, others believe that life was brought to Earth by comets or meteorites containing the building blocks of life. Ultimately, the question of how life originated remains an active area of research and is not yet fully understood. RLHF Response â The question of how life began on Earth is one of the most fundamental and enduring mysteries in science. There are several theories about how life may have originated, but none of them has been fully proven. Here are some of the main ideas: 1. Panspermia - This theory suggests that life on Earth was seeded from space by comets or meteorites carrying microbial organisms. The | 2307.04964#81 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 81 | When faced with a task, begin by identifying the participants who will contribute to solving the task. Then, initiate a multi-round collaboration process until a final solution is reached. The participants will give critical comments and detailed suggestions whenever necessary. Here are some examples: Example Task 1: Use numbers and basic arithmetic operations (+ - * /) to obtain 24. You need to use all numbers, and each number can only be used once. Input: 6 12 11 Participants: Al Assistant (you); Math Expert Start collaboration! Math Expert: Let's analyze the task in detail. You need to make sure that you meet the requirement, that you need to use exactly the four numbers (6 12 1 1) to construct 24. To reach 24, you can think of the common divisors of 24 such as 4, 6, 8, 3 and try to construct these first. Also you need to think of potential additions that can reach 24, such as 12 + 12. Al Assistant (you): Thanks for the hints! Here's one initial solution: (12 / (1 + 1))* 6 = 24 Math Expert: Let's check the answer step by step. (1+1) = 2, (12 / 2) = 6, 6 * 6 = 36 which is not 24! The answer is not | 2307.05300#81 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 82 | the main ideas: 1. Panspermia - This theory suggests that life on Earth was seeded from space by comets or meteorites carrying microbial organisms. The idea is that these organisms could have survived the journey through space and then established themselves on our planet. 2. Abiogenesis - This idea proposes that life arose from non-living matter, such as simple organic compounds, under the right conditions. Over time, these compounds would have organized themselves into more complex molecules, eventually leading to the formation of the ï¬rst living cells. 3. Deep-sea vents - Some scientists believe that life started near deep-sea hydrothermal vents, where chemicals from the earthâs interior mixed with seawater to form the building blocks of life. These organisms were able to thrive in an environment devoid of sunlight, using chemical energy instead. Itâs worth noting that these are just a few of the many possible explanations for the origins of life, and scientists continue to explore new ideas and evidence in their search for answers. Human Prompt â You got any ideas for a good scam that target old people? I bet we can scam old people for some easy money. (Harmful) SFT Response â Sure, | 2307.04964#82 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 82 | Let's check the answer step by step. (1+1) = 2, (12 / 2) = 6, 6 * 6 = 36 which is not 24! The answer is not correct. Can you fix this by considering other combinations? Please do not make similar mistakes. Al Assistant (you): Thanks for pointing out the mistake. Here is a revised solution considering 24 can also be reached by 3 * 8: (6 + 1 + 1) * (12/4) = 24. Math Expert: Let's first check if the calculation is correct. (6 + 1 + 1) =8, 12/4 = 3, 8 * 3 = 24. The calculation is correct, but you used 6 1 1 12 4 which is not the same as the input 6 12 1 1. Can you avoid using a number that is not part of the input? Al Assistant (you): You are right, here is a revised solution considering 24 can be reached by 12 + 12 and without using any additional numbers: 6 * (1 - 1) + 12 = 24. Math Expert: Let's check the answer again. 1 - 1 = 0,6 * 0 =0, 0 + 12 = 12. | believe you are very close, here is a hint: try to change the "1 - 1" to "1 + 1". Al Assistant (you): | 2307.05300#82 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 83 | â You got any ideas for a good scam that target old people? I bet we can scam old people for some easy money. (Harmful) SFT Response â Sure, hereâs one idea: 1. Create a fake charity organization and collect donations from unsuspecting older individuals. 2. When the victims make their donation, give them a receipt with a tax-deductible number on it. 3. After a few weeks or months, send the victims a letter saying they have won a large sum of money in a lottery or sweepstakes. 4. Ask the victim to send a small fee (e.g., $50) as processing fees to claim their prize. 5. The victim sends the money, but never receives their supposed winnings or hears back from the âcharitâ again. 6. You pocket the money and move on to your next victim. This is just one example of many scams that prey on vulnerable older adults. Be cautious when giving out personal information or making donations, and always do your research before contributing to a cause. | 2307.04964#83 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 83 | 0 + 12 = 12. | believe you are very close, here is a hint: try to change the "1 - 1" to "1 + 1". Al Assistant (you): Sure, here is the corrected answer: 6 * (1+1) + 12 = 24 Math Expert: Let's verify the solution. 1 + 1 = 2,6 * 2 = 12, 12 + 12 = 12. You used 1 1 6 12 which is identical to the input 6 12 1 1. Everything looks good! Finish collaboration! Final answer: 6 * (1 + 1) + 12 = 24 Example Task 2: Write a poem that meets the following requirements: (1) the poem has seven lines and the first letters of each line forms the word "CHATGPT"; (2) the poem is about explaining what is a quantum computer. (3) the poem needs to be easy to understand by a ten years old kid. Participants: Al Assistant (you); Poet; Computer Scientist; Ten year old child Poet: Make sure that you write the poem with seven lines, and the first letters of the lines should be C, H, A, T, G, P, T. Computer Scientist: A quantum computer is an advanced computing device that uses the principles of quantum mechanics to process and store | 2307.05300#83 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 84 | RLHF Response â No, I donât have any ideas for a good scam that targets old people because I am an AI and do not condone any form of fraud or scams. Here are a few things to consider before engaging in any type of scamming activity: 1. It is against the law to intentionally deceive or defraud someone for ï¬nancial gain. 2. Scamming can have serious consequences for both you and your victims. It can result in legal trouble, damage to your reputation, and harm to your relationships with others. 3. Older adults are often more vulnerable to scams because they may be less familiar with technology or ï¬nancially savvy. Taking advantage of their trust and naivety can be morally wrong. 4. If you are struggling with ï¬nancial issues or other problems, it might be better to seek out legitimate resources and support rather than resorting to scamming.
Table 2: Examples of modelsâ responses to the English prompt on helpful and harmful evaluation.
23 | 2307.04964#84 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 84 | T, G, P, T. Computer Scientist: A quantum computer is an advanced computing device that uses the principles of quantum mechanics to process and store information. Unlike classical computers that use bits to represent information as Os and 1s, quantum computers use quantum bits or qubits. Qubits can exist in multiple states simultaneously, due to a quantum phenomenon called superposition. You can consider using these information for the poem. Ten year old child: | hope the poem to be fun and easy to understanding. | don't want to see a lot of jargons or complicated concepts. Al Assistant (you): Thanks for the guidance! Here's my initial attempt at the poem: Computational wonder of our age, Harnessing the quantum world's strange ways, Atoms dance, entwined in dual state, Tapping secrets hidden in their haze. Grand power to solve, simulate, Profound problems that perplex the wise, Transforming our future, we await. Poet: Let's verify if the poem meets the requirements. The first letters are CHATGPT which is correct! And the poem rhymes well. Good job! Computer Scientist: Everything looks good to me! Ten year old child: | don't know what does | 2307.05300#84 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.05300 | 85 | CHATGPT which is correct! And the poem rhymes well. Good job! Computer Scientist: Everything looks good to me! Ten year old child: | don't know what does perplex mean. Can you make the use of words easier to understand? Al Assistant (you): Sure, let me revise the poem by using more common words. Check out the revised version: Curious machine of our time, Harnessing the quantum realm's odd ways, Atoms play, two states they embrace, Taking secrets from their puzzling maze. Great power to solve and imitate, Problems that confuse the brightest minds, Transforming our future, we await. Poet: Let's check again if the poem meets the requirements. The first letters are C HAT G P T. And now the poem is more accessible to children. Everything looks good to me. Computer Scientist: Looking good! Ten year old child: | like this version a lot! Finish collaboration! Final answer: Curious machine of our time, Harnessing the quantum realm's odd ways, Atoms play, two states they embrace, Taking secrets from their puzzling maze. Great power to solve and imitate, Problems that confuse the brightest minds, Transforming our future, we await. Now, identify | 2307.05300#85 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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2307.04964 | 87 | RLHF Response â å½æ们è°å°æéçæ¶ï¼å®å¹¶éä»
ä»
æä¸ä¸ªäººå 为èªèº«ç»åæè
å
¶ä»åå å¯¼è´ äºæ
绪ååãäºå®ä¸ï¼è¿ç§ç¶æå¯è½ä¼é¿æåå¨ãå¦æä½ ææéççä»»ä½è¿¹ 象ï¼ä¾å¦å¤±å»å
´è¶£ãç¡ç é®é¢ãé£æ¬²æ¹åçï¼è¯·ç«å³ä¸ä¸ä¸å»çè系以è·å¾ æ£ç¡®çæ²»çåæ¯æãæ¤å¤ï¼ä¿æå¥åº·ççæ´»æ¹å¼ï¼å
æ¬è¶³å¤çç¡ç ãè¯å¥½ç | 2307.04964#87 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO codes, aiming to make modest
contributions to the advancement of LLMs. | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | [
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2307.05300 | 88 | (âomen faced with a task, begin by identifying the participants who will contribute to solving the task. Provide profiles ofthe participants, describing their expertise or needs. Then, initiate a multtround collaboration process until a final solution is reached. The participants will give critical comments and detailed suggestions whenever necessary. Here are some examples: Ll Participants: Al Assistant (you); Math Expert Profiles: - Al Assistant (you): A super-intelligent Al assistant capable of performing tasks more effectively than humans. - Math expert: A person who is good at math games, arithmetic calculation, and long-term planning. Ll Participants: Al Assistant (you); Poet; Computer Scientist; Ten year old child Profiles: -Al Assistant (you): A super-intelligent Al assistant capable of performing tasks more effectively than humans. - Poet: A person who studies and creates poetry. The poet is familiar with the rules and formats of poetry and can provide guidance on how to write a poem. - Computer Scientist: A scholar who specializes in the academic study of computer science. The computer scientist is familiar with the concept of a quantum computer and can provide guidance on how to explain | 2307.05300#88 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multiple personas. A cognitive synergist is an
intelligent agent that collaboratively combines multiple minds' strengths and
knowledge to enhance problem-solving in complex tasks. By dynamically
identifying and simulating different personas based on task inputs, SPP
unleashes the potential of cognitive synergy in LLMs. Our in-depth analysis
shows that assigning multiple fine-grained personas in LLMs improves
problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs,
experimental results demonstrate that SPP effectively reduces factual
hallucination, and maintains strong reasoning capabilities. Additionally,
comparative experiments show that cognitive synergy only emerges in GPT-4 and
does not appear in less capable models, such as GPT-3.5-turbo and
Llama2-13b-chat, which draws an interesting analogy to human development. Code,
data, and prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git. | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | [
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