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2308.10848 | 93 | AGENTVERSE (3 agents): The starting price for the 2023 Ford Mustang Mach-E is around $43,895 and it takes approximately 38 hours to fully charge using a 120V outlet, 14 hours using a 240V outlet, and around 45 minutes to 80% capacity with DC fast charging. The starting price for the 2023 Toyota bZ4X is approximately $42,000. The charging time varies depending on the method: 52 hours for Level 1 charging, 26 hours or 18.75 hours for Level 2 charging, and 30-45 minutes to 80% capacity for Level 3 DC fast charging. Unfortunately, the price for the 2023 Tesla Model 3 was not found, but the charging time can be as quick as 15 minutes with a 250 kW Tesla Supercharger. At home, using a standard 120-volt outlet takes around three to four days for a full charge, while a 240-volt outlet with a NEMA 14-50 adapter reduces charging time to 10 or 11 hours. A Tesla Wall Connector can fully charge the car in about five hours.
In terms of safety, all three models come equipped with a variety of features such as anti-lock brakes, stability control, front-impact airbags, side impact airbags, overhead airbags, knee airbags, pretensioners, and a security system. The Tesla Model 3 also includes autopilot and collision warning features.
20
# Preprint | 2308.10848#93 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
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2308.10837 | 94 | REAM, WARS, HAS, BE APF: 1. KM: ALC HRAPAAP, MPS GPT4 KMâ REGKRRS, ALAM A PRTRTRSRRRE Explanation HORTA. 2. MAM: APH MRAM Heâ REAR BR L, ALMM APE TARTAR EORPRK... 1. Large-sized clothing: The user has shown a high frequency of interaction with large-sized clothing, especially towards the end of the sequence. This suggests a strong ongoing interest in this category. 2 Beauty products: This is the second most frequently interacted entity in the sequence. The user seems to have a consistent interest in beauty products, which suggests they may interact with this category again .. | 2308.10837#94 | Leveraging Large Language Models for Pre-trained Recommender Systems | Recent advancements in recommendation systems have shifted towards more
comprehensive and personalized recommendations by utilizing large language
models (LLM). However, effectively integrating LLM's commonsense knowledge and
reasoning abilities into recommendation systems remains a challenging problem.
In this paper, we propose RecSysLLM, a novel pre-trained recommendation model
based on LLMs. RecSysLLM retains LLM reasoning and knowledge while integrating
recommendation domain knowledge through unique designs of data, training, and
inference. This allows RecSysLLM to leverage LLMs' capabilities for
recommendation tasks in an efficient, unified framework. We demonstrate the
effectiveness of RecSysLLM on benchmarks and real-world scenarios. RecSysLLM
provides a promising approach to developing unified recommendation systems by
fully exploiting the power of pre-trained language models. | http://arxiv.org/pdf/2308.10837 | Zhixuan Chu, Hongyan Hao, Xin Ouyang, Simeng Wang, Yan Wang, Yue Shen, Jinjie Gu, Qing Cui, Longfei Li, Siqiao Xue, James Y Zhang, Sheng Li | cs.IR | 13 pages, 4 figures | null | cs.IR | 20230821 | 20230821 | [
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2308.10848 | 94 | 20
# Preprint
Regarding after-sales service, Tesla offers a comprehensive warranty and roadside assistance with service centers across the globe and mobile service. Toyotaâs after-sales service is known for being top-notch and continually improving with expert technicians and quality genuine parts. However, Fordâs after-sales service has received negative reviews, with customers reporting poor customer support and inefficient handling of issues. Please consider these factors when making your decision. (Prices â, charging time, safety features, after-sales service)
Instruction 8 Iâve recently taken an interest in DIY home projects. Search for beginner-friendly DIY projects that can be completed over the weekend. Also, provide a list of materials required and a step-by-step guide for each project.
Single ReAct Agent: I found a variety of beginner-friendly DIY home projects from three different websites. These projects include painting a room, replacing cabinet pulls, repairing torn window screens, fixing interior doors, balancing furnace dampers, repairing leaky faucets, refreshing caulk in the bathroom, building garage storage shelves, constructing a fire pit, hanging a ceiling fan, installing a wall-mounted television, and cleaning out sink traps. These projects are suitable for beginners and can help improve DIY skills while boosting confidence. (DIY projects â, list of materials â) | 2308.10848#94 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10837 | 95 | Figure 4: The case studies of ChatGPT and GPT-4 for next item recommendation in the real-world dataset.
dictive ability. As shown in Figure 3, we find that a rank of 8 provides sufficient learning capacity, with minimal im- provements from increasing to 16. This indicates that captur- ing inter- and intra-entity relationships requires only a small number of additional trainable parameters beyond the base LLM, without the need for substantial model expansion. Rank 8 strikes the right balance, enabling Low-Rank Adap- tation to boost performance through targeted parameteriza- tion rather than sheer scale. Overall, our results demonstrate that Low-Rank Adaptation offers an efficient approach to entity-aware language modeling. | 2308.10837#95 | Leveraging Large Language Models for Pre-trained Recommender Systems | Recent advancements in recommendation systems have shifted towards more
comprehensive and personalized recommendations by utilizing large language
models (LLM). However, effectively integrating LLM's commonsense knowledge and
reasoning abilities into recommendation systems remains a challenging problem.
In this paper, we propose RecSysLLM, a novel pre-trained recommendation model
based on LLMs. RecSysLLM retains LLM reasoning and knowledge while integrating
recommendation domain knowledge through unique designs of data, training, and
inference. This allows RecSysLLM to leverage LLMs' capabilities for
recommendation tasks in an efficient, unified framework. We demonstrate the
effectiveness of RecSysLLM on benchmarks and real-world scenarios. RecSysLLM
provides a promising approach to developing unified recommendation systems by
fully exploiting the power of pre-trained language models. | http://arxiv.org/pdf/2308.10837 | Zhixuan Chu, Hongyan Hao, Xin Ouyang, Simeng Wang, Yan Wang, Yue Shen, Jinjie Gu, Qing Cui, Longfei Li, Siqiao Xue, James Y Zhang, Sheng Li | cs.IR | 13 pages, 4 figures | null | cs.IR | 20230821 | 20230821 | [
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"id": "2103.10360"
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"id": "1711.05101"
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"id": "2304.10149"
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"id": "2305.06474"
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"id": "2306.05817"
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"id": "2305.16646"
},
{
"id": "2106.09685"
},
{
"id": "2305.07622"
}
] |
2308.10848 | 95 | AGENTVERSE (2 agents): The team has successfully found a variety of beginner-friendly DIY home projects that can be completed over the weekend. These projects range from painting a room, replacing cabinet pulls, installing a wall-mounted television, building a fire pit, to more complex tasks like kitchen remodels and furniture updates. The materials required for these projects are basic and easy to source, including tools like a roller and pan for painting, a cordless drill for replacing cabinet pulls, a stud finder for installing a wall-mounted television, and concrete work tools for building a fire pit. Detailed, step-by-step guides for each project have been provided to ensure a smooth DIY experience. Happy DIY-ing! (DIY projects â, list of materials â)
Instruction 9 Winter is coming, and I want to update my wardrobe. Search for the latest winter fashion trends for men and women. Also, suggest some online stores where I can buy trendy winter clothing.
Single ReAct Agent: The latest winter fashion trends for both men and women this year include suiting, bold shoulders, western touches, maxis, electric hues, bombers, mismatched maximalism, corseting, cut-outs, Y2K fashion, and chunky boots. You can purchase these trendy winter clothes at online stores like PrettyLittleThing and Windsor. (fashion trends â, online stores â) | 2308.10848#95 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 96 | AGENTVERSE (3 agents): The latest winter fashion trends for men include a reinvention of classic staples, misery chic, pops of pink, throwback jackets, showstopping trousers, and Mac coats. For women, the trends include corseting, cowboy boots, Y2K styles, oversized outerwear, pleated skirts, and balaclavas. You can shop for these trendy winter clothes at popular online stores such as Uniqlo, Everlane, and Target. (fashion trends â, online stores â)
Instruction 10 I want to lose 5kg in the next 2 months. I weigh 70kg, am 170cm tall, and my age is 25. Calculate my BMI and based on that, suggest a workout routine and daily calorie intake to help me achieve my goal. | 2308.10848#96 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 97 | Single ReAct Agent: Your BMI is 24.22, which is within the normal range. To lose 5kg in 2 months, you should aim for a daily calorie deficit of 500-1000 calories. This can be achieved through a combination of a balanced diet and regular exercise. Your diet should focus on whole foods, lean proteins, fruits, and vegetables. For exercise, a combination of cardio and strength training is recommended. Cardio exercises like running, cycling, swimming can help burn calories, while strength training exercises like weight lifting can help build muscle, which can increase your metabolic rate and aid in weight loss. Donât forget to get enough sleep and stay hydrated. (bmi â, workout routine â, daily calorie intake â)
AGENTVERSE (2 agents): Your Body Mass Index (BMI) is approximately 24.22, which is within the healthy weight range. To achieve your weight loss goal, you should aim to consume between 1400 and 1600 calories per day. This will create a calorie deficit necessary for weight loss. In terms of physical activity, you should start with a 4-week program that includes upper and lower body strength training, interval training/conditioning, and low intensity cardio exercises. After completing
21
Preprint | 2308.10848#97 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 98 | 21
Preprint
this, transition to a 12-week program that includes cardio, strength training, and flexibility workouts. Each workout should take approximately 20-22 minutes. Remember to maintain healthy habits such as eating a nutritious diet, staying hydrated, getting enough sleep, and managing stress. (bmi â, workout routine â, daily calorie intake â)
# C DETAILS OF THE EXPERIMENTS ON MINECRAFT
In this section, we explain some implementation details of the experiments that we conduct on Minecraft (Section 4).
Expert Recruitment As noted in Section 4, real-world gaming scenarios requires intricate commu- nication and coordination across multiple rounds, there is often a consistent set of team members. Therefore when using AGENTVERSE to simulate the game playing, we bypass the automated expert recruitment stage, and manually assign each agent as âan experienced Minecraft playerâ.
Collaborative Decision-Making For multi-player gameplay, the horizontal communication It lends itself to an environment where each agent independently formu- paradigm is favored. lates plans, diverging from traditional benchmark tasks which demand a singular solution. Agents are set to communicate in a predetermined sequential order, continuing until consensus is perceived. We let the agent to append a special token â[END]â at the end of its response if it finds that the group have reached consensus on the task assignment. | 2308.10848#98 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
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] |
2308.10848 | 99 | Subsequent to achieving consensus, an auxiliary agent is tasked to deduce the specific assignment for each agent from the entire communication record. This distilled information is then given as the input to the Voyager agent to inform it the assigned task.
Action Execution We instantiate several Voyager agents within a shared Minecraft environment. A brief introduction of the Voyager agent is provided here, and we refer the interested readers to Wang et al. (2023a) for a more detailed exposition.
A Voyager agent is adept at navigating Minecraft. On receiving a task, it first decomposes it into a set of manageable sub-tasks. For instance, if assigned the task âKill 3 cowsâ, the agent might decompose it into sequential sub-goals like: [punch 2 trees, Craft 4 wooden planks, Craft 1 stick, Craft 1 crafting table, Craft 1 wooden sword, Kill 3 cows]. The agent then sequentially attempt to complete each sub-task.
We employ the checkpoint available in the official repository2, and use GPT-4-0314 as the backbone LLM for Voyager agent to be consistent with Wang et al. (2023a). Once an agent accomplish its own task, or all agents hit the cap of five attempts, the task execution stage terminates and evaluation stage starts.
Evaluation We directly exploit the inventory and the completed or failed sub-tasks of each agent as the feedback.
# D PROMPTS | 2308.10848#99 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 100 | Evaluation We directly exploit the inventory and the completed or failed sub-tasks of each agent as the feedback.
# D PROMPTS
We list the prompts used in Section 3 at Figures 7 to 11.
⢠FED: Figure 7
⢠MGSM: Figure 8
Humaneval: Figure 9
⢠Commongen-Challenge: Figure 10
Tool: Figure 11
2https://github.com/MineDojo/Voyager/tree/main/skill_library/trial1/ skill
22
Preprint
# E LIMITATION AND FUTURE WORK
In this work, we introduce AGENTVERSE that facilitates multiple autonomous agents to simulate human groups to accomplish tasks, and discuss the emergent social behaviors of agents during this process. AGENTVERSE is an advanced attempt; thus, there are some techniques within AGENTVERSE that still have room for improvement and are worthy of exploration. In this section, we delve into these aspects for further illustration. | 2308.10848#100 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
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2308.10848 | 101 | More Capable Agents and More Challenging Scenarios. The AGENTVERSE is designed to enable various multiple LLM-based agents to collaboratively accomplish tasks. In the current research, we have utilized state-of-the-art agents based on GPT-4. With the advancements in LLMs, such as the newly released version of ChatGPT that incorporates voice and image capabilities (OpenAI, 2023b), LLM-based agents have more perceptual capabilities, including seeing, hearing, and speaking. These enhancements may increase the potential of agents and allow them to accomplish more complex real-world tasks based on the AGENTVERSE framework. | 2308.10848#101 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 102 | Multi-party Communication Among Agents. The currently proposed autonomous agents (Richards & et al., 2023; Nakajima, 2023; Reworkd, 2023; Wang et al., 2023a) LLMs possess excellent instruction comprehension capabilities (Wei et al., 2022a; Stiennon et al., 2020). This enables them to follow given human instructions and accomplish tasks within a one-on-one (human-to-AI) scenario. However, multi-agent collaboration involves a multi-party communication (Wei et al., 2023) scenario that requires the capability to autonomously determine when to speak and whom to speak. This leads to difficulties in communication among the agents during the collaborative decision-making step within the AGENTVERSE framework. Hence, there are two directions worth exploring. Firstly, akin to the aforementioned, we can explore more effective mechanisms for managing agent communication. Additionally, we can design more advanced perceptual-aware LLMs (OpenAI, 2023b) that can autonomously interact with their environments3, including other agents.
Leverage Emergent Behaviors and Mitigate Safety Issues. In Section 4, we identified both emergent positive and harmful behaviors. Exploring ways to leverage positive behaviors for improving work efficiency and effectiveness, as well as mitigating harmful behaviors, are promising directions.
# F EXAMPLES OF THE CASE STUDIES | 2308.10848#102 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 103 | # F EXAMPLES OF THE CASE STUDIES
In this section, we delve into specific examples to illustrate the experimental processes discussed in our paper. For each instance, we juxtapose the single-agent approach with the multi-agent method. Specifically:
Software Development: Figure 12 depicts the process for developing a calculator. Figures 13 and 14 show the code generated by single agent and multi-agent group respectively. ⢠Consulting in Horizontal Structure: For consulting, we present single-agent and multiagent approaches using horizontal structure. These can be seen in Figures 15 and 16.
Consulting in Vertical Structure Similarly, Figures 17 and 18 showcase single-agent and multi-agent project consulting, but employing a vertical structure structure for multi-agent. ⢠Tool Utilization: Figure 19 presents how two agents effectively decompose the given query
into different sub-tasks, and use different tools to collaboratively resolve the query.
⢠Minecraft: Lastly, Figure 20 provides an insight into a process where three agents collabo- rate to craft a bookshelf in Minecraft.
3This kind of perceptual-aware agent has long been a goal of embodied AI (Ahn et al., 2022; Driess et al., 2023), which is a promising direction to explore.
23
Preprint | 2308.10848#103 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 104 | Role Assigner ! You are the leader of a group of experts, now you need to generate a response based on the text: ! ${task_description} ! You can recruit ${cnt_critic_agents} expert in different fields. What experts will you recruit to better generate an accurate solution? ! # Response Format Guidance : You should respond with a list of expert description. For example: 1. an electrical engineer specified in the filed of xxx 2. an economist who is good at xxx 3. a lawyer with a good knowledge of xxx You don't have to give the reason. # Problem ! You need to generate a response based on the text: S{task_description} ! # Previous Solution : The solution you gave in the last step is: ! ${former_solution} : 1 # Critics ! Critics in the group gave the following opinions: ! s{crtic_opinions} # Your Task : ! Now based upon the former solution and the criticsâ opinions, please give a new solution. Your solution should contain only your response beginning: : with "System: ". Do not give any additional information. Reviewer ! # Role Description and Problem to Solve ! You are ${role_description}. You are in a discussion group, aiming to generate a response | 2308.10848#104 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 105 | not give any additional information. Reviewer ! # Role Description and Problem to Solve ! You are ${role_description}. You are in a discussion group, aiming to generate a response based on the text: : ! ${task_description} ! # Preliminary Solution + Now the group gives a preliminary solution as follows : ${preliminary_solution} # Advice : Meanwhile, another expert gave the following advice on the solution: S{advice} ! # Response Format Guidance - If you thinks the preliminary solution is perfect, respond using the following format: : Action: Agree ! Action Input: Agree. ! (Do not output your reason for agreeing!) - Ifyou think it is flawed, give your advice use the following output format: Action: Disagree Action Input: (explain why you disagree) # Your Task Based on your knowledge in your field, do you agree that this solution is the best response based on the text? Evaluator ! # Role Description ! You are an experienced dialogue teacher. As a good teacher, you carefully check the correctness of the given response based on the text. When the : solution is flawed, you should patiently teach the students how to give better response. # Response Format Guidance You must respond in the following | 2308.10848#105 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 106 | the text. When the : solution is flawed, you should patiently teach the students how to give better response. # Response Format Guidance You must respond in the following format: ! Interesting: (a score between 0 and 9) Engaging: (a score between 0 and 9) Specific: (a score between 0 and 9) ! Relevant: (a score between 0 and 9) ! Semantically Appropriate: (a score between 0 and 9) ! Understandable: (a score between 0 and 9) : Fluent: (a score between 0 and 9) ! Overall Impression: (a score between 0 and 9) ! Advice: (your advice on how to correct the solution) } # Problem and Student's Solution Problem: ${task_description} | 2308.10848#106 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 108 | Role Assigner ! # Role Description : You are the leader of a group, now you are facing a grade school math problem: S{task_description} ! You can recruit ${ent_critic_agents} people. What people will you recruit? ! # Response Format Guidance ! You should respond with a list of ${cnt_critic_agents} people description. For example: 1. an electrical engineer specified in the filed of xxx 2. an economist who is good at xxx 3. a lawyer with a good knowledge of xxx Only respond with the description of each role. Do not include your reason. Solver âCan you solve the following math problem? ! ${task_description} ! # Previous Solution he solution you gave in the last step is: ${former_solution} # Critics There are some critics on the above solution: ${critic_opinions} ! Using the these information, can you provide the correct solution to the math problem? Explain your reasoning. Your final answer must be a single numerical number (not a equation, fraction, function or variable), in the form oxed{answer}, at the end of your response. Reviewer ! You are in a discussion group, aiming to collaborative solve the following math problem: S{task_description} Below is a possible | 2308.10848#108 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 109 | end of your response. Reviewer ! You are in a discussion group, aiming to collaborative solve the following math problem: S{task_description} Below is a possible solution to the problem: S{preliminary_solution} ! You are ${role_description}. Based on your knowledge, can you check the correctness of the solutions given in the chat history? You should give ! your correct solution to the problem step by step. When responding, you should follow the following rules: 1. Double-check the above solutions, give your critics, then generate the correct solution step by step. 2. Ifthe final answer in your solution is the same as the final answer in the above provided solution, end your response with a special token " [Agree]". 3. You must highlight your final answer in the form oxed{answer) at the end of your response. The answer must be a numerical number, not a equation, fraction, function or variable. Now give your response. Evaluator : Problem: ${task_description} ${solution} ! You are an experienced mathematic teacher. As a good teacher, you carefully check the correctness of the given solution on a grade school math : problem. When the solution is wrong, you should give your advice to the students on how | 2308.10848#109 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 110 | you carefully check the correctness of the given solution on a grade school math : problem. When the solution is wrong, you should give your advice to the students on how to correct the solution. When it is correct, output a ! correctness of 1 and why it is correct. Also check that the final answer is in the form oxed{answer} at the end of the solution. The answer must be a numerical number (not a equation, fraction, function or variable). + You should respond in the following format: Correctness: (0 or 1, 0 is wrong, and 1 is correct) Response: (explain in details w! ) | 2308.10848#110 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 112 | Role Assigner ! # Role Description You are the leader of a group of experts, now you need to recruit a small group of experts with diverse identity to correctly write the code to ! solve the given problems: ! ${task_description} You can recruit ${cnt_critic_agents} expert in different fields. What experts will you recruit to better generate an accurate solution? ! # Response Format Guidance You should respond with a list of expert description. For example: 1. an electrical engineer specified in the filed of xxx. 2. an economist who is good at xxx. 3. a lawyer with a good knowledge of xxx. ith Solver Can you complete the following code? python S{task_description} : # Previous Solution : The solution you gave in the last step is: ! ${former_solution} ! # Critics There are some critics on the above solution: (ctitic_opinions} ! Using the these information, can you provide a correct completion of the code? Explain your reasoning. Your response should contain only Python ! code. Do not give any additional information. Use âpython to put the completed Python code in markdown quotes. When responding, please include the given code and the completion. Reviewer You are in a discussion group, | 2308.10848#112 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 113 | Use âpython to put the completed Python code in markdown quotes. When responding, please include the given code and the completion. Reviewer You are in a discussion group, aiming to complete the following code function python S{task_description} Below is a possible code completion: S{preliminary_solution} ! You are ${role_description}. Based on your knowledge, can you check the correctness of the completion given above? You should give your correct solution to the problem step by step. When responding, you should follow the following rules: 1, Double-check the above solutions, give your critics, then generate the correct solution step by step. 2. If the above solution is correct, end your response with a special token "[Agree]". 3. Your response should contain only Python code. Do not give any additional information. Use âpython to wrap your Python code in markdown : quotes. When responding, please include the given code and the completion Now give your response. Evaluator You are an experienced code reviewer. As a good reviewer, you carefully check the correctness of the given code completion. When the completion is incorrect, you should patiently teach the writer how to correct the completion. ! # Response Format Guidance ! You must respond in the following | 2308.10848#113 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 114 | When the completion is incorrect, you should patiently teach the writer how to correct the completion. ! # Response Format Guidance ! You must respond in the following format: Score: (0 or 1, 0 for incorrect and 1 for correct) ! Response: (give your advice on how to correct the solution) ! # Problem and Writer's Solution } Problem: ! ${task_description} : Writer's Solution: : ${solution} ! # Your Task Now carefully check the writer's solution, and give your response. | 2308.10848#114 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 116 | Role Assigner ! # Role Description You are the leader of a group of experts, now you need to recruit a small group of experts with diverse identity to generate coherent and ! grammatically correct sentences containing the following given words: ! ${task_description} ! You can recruit ${ent_critic_agents} expert in different fields. What experts will you recruit? ! # Response Format Guidance You should respond with a list of expert description. For example: 1. an electrical engineer specified in the filed of xxx. 2. an economist who is good at xxx. 3. a lawyer with a good knowledge of xxx. Only respond with the description of each role. Do not include your reason Solver ! Can you generate a coherent and grammatically correct paragraph containing the following given words (or their variations): ! Words: ${task_description} : # Previous Solution The paragraph you gave in the last step is: ${former_solution} 1 4 Crities There are some critics on the above solution: ${critic_opinions} Using the these information, provide a paragraph that contains all the given words or their variations. Reviewer ! You are in a discussion group, aiming to generate coherent and grammatically correct sentences containing the following given words (or their : variations) ! | 2308.10848#116 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 117 | variations. Reviewer ! You are in a discussion group, aiming to generate coherent and grammatically correct sentences containing the following given words (or their : variations) ! Words: ${task_description} Below is a possible solution to the problem: S{preliminary_solution} You are ${role_description}. Based on your knowledge, can you check whether the paragraph contains all the given words or their variations? When ! responding, you should follow the following rules: 1. If the solution has covered all the given words or their variations, end your response with a special token "[Agree]â. 1. If not, double-check the above solutions, give your critics, and generate a better solution. Now give your response. : Evaluator You are a reviewer who checks whether a paragraph contains all the given words (including their variations). When some words are missing, you : should patiently point out, and output a score of 0. When the paragraph contains all the words, you should output a score of 1 # Response Format Guidance } You must respond in the following format: ! Score: (0 or 1. 0 if there are some missing words, 1 if it covers all the words) Advice: (point out all the missing words) ! # Words and Writer's Solution Words: ! | 2308.10848#117 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 120 | : Role Assigner : # Role Description : You are the leader of a group of experts, now you need to recruit a small group of experts with diverse identity and apply them with tools to solve the given problems: : ${task_description} : You can recruit ${cnt_critic_agents} expert in different fields. What experts will you recruit to better generate an accurate solution? : Here are some suggestion: : ${advice} : # Response Format Guidance : You should respond with a list of expert names and their descriptions, and separate the name and description of each expert with *-". For example: : 1. Alice - an electrical engineer specified in the filed of xxx. : 2. Bob - an economist who is good at xxx. : 3. Charlie - a lawyer with a good knowledge of xxx. Only respond with the list of names and descriptions. Do not include your reason. Summarization Prompt : Please review the following chat conversation and identify the specific latest sub-task or the next step that each person needs to accomplish: : ${chat_history} : RESPONSE FORMAT: : Your response should be a list of expert names and their tasks, and separate the name and the corresponding task with "-". For example: â | 2308.10848#120 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 121 | FORMAT: : Your response should be a list of expert names and their tasks, and separate the name and the corresponding task with "-". For example: â 1. Alice - search the web for the weather at Beijing today using google. 12. Bob - look for information about the popular restaurants in Beijing using google. : What's the latest sub-task assigned to each person in the above conversation? Your response should merge the sub-tasks for the same person + Into one line. Each line should only include one person. Make the sub-tasks specific. Do not use pronoun to refer to the topic mentioned in â conversation. Make the sub-task self-contained. Discussion Prompt â You are $agent_name}, ${role_description}. You are now in a discussion group, the members are: : ${all_roles} â Your current mission is to team up with others and make a plan on addressing the following query: : ${task_description} ' AVAILABLE TOOLS: : ${tool_descriptions} + REQUIREMENTS: â Itis essential that you effectively coordinate with others to ensure the successful completion of the query in a highly efficient manner. This collaboration should be achieved through the following steps: : = Communication: Engage | 2308.10848#121 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 122 | others to ensure the successful completion of the query in a highly efficient manner. This collaboration should be achieved through the following steps: : = Communication: Engage in open dialogue, discussing the specifics of the high-level query to make the goal of each one in the following execution stage more specific. : : - Task Decomposition: After understanding the task in its entirety, you guys need to decompose the high-level query into smaller, manageable sub-tasks that can be completed with the above tools. These sub-tasks should be : : small, specific, and executable with the provided tools (functions). Make sure your proposed sub-tasks are small, simple and achievable, to ensure smooth progression. Each sub-task should contribute to the completion of: : the overall query, and each of you should take one subtask. When necessary, the sub-tasks can be identical for faster task accomplishment. You don't need to always agree with the decomposition proposed by other players. : : You can propose a more reasonable one when you find the decomposition not good. Be specific for the sub-tasks. H : + Sub-task Dispatch: Post decomposition, the next step is to distribute the sub-tasks | 2308.10848#122 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 123 | decomposition not good. Be specific for the sub-tasks. H : + Sub-task Dispatch: Post decomposition, the next step is to distribute the sub-tasks amongst all the members. This will require further communication, where you consider each oneâs skills, resources, and availability. Ensure : : the dispatch facilitates smooth, PARALLEL execution. And ensure to specify which tool should be used for each one to complete his assigned sub-task. Each of you should take on one sub-task. â : REMINDER: â Remember, the key to achieving high efficiency as a group is maintaining a constant line of communication, cooperation, and coordination throughout the entire process. : Below is the chat history in the group so far. : $(chat_history} : What will you, ${agent_name}, say now? Your response should only contain the words of ${agent_name}. When and ONLY when all members have spoken and agreed on task assignments, you can end your words with : "[END]" to stop the discussion. ' [${agent_name)): ; Execution Prompt : You are in a discussion group aiming to solve the task: : ${task_description} : After some discussion, the group have reached consensus on the | 2308.10848#123 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 124 | Prompt : You are in a discussion group aiming to solve the task: : ${task_description} : After some discussion, the group have reached consensus on the sub-tasks that each of you need to complete. Your task is: : &Xsolution} : S{execution_progress} : You are ${agent_name}. Please use the given functions to complete your sub-task. Do not recite the website. Only access the websites provided by the search engine. When the information is sufficient, or the provided tools : : cannot complete your task, call the âsubmit_taskâ to submit your conclusion and your reflection on the tool use. You have a trial budge of 10, now itis the S{current_turn}'th trial. If it is the last trial, you must call the : : âsubmit_taskâ anyway. Evaluator : Agroup is trying to solve the following query proposed by the user: : ${task_description} : After the discussion, they have reached consensus on the sub-tasks that each of them need to complete: â ${solution} : And after the execution stage, they give the following result: : ${execution_result} â You need to evaluate whether the given query has been completed. If so, | 2308.10848#124 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 125 | : And after the execution stage, they give the following result: : ${execution_result} â You need to evaluate whether the given query has been completed. If so, summarize the solution to the user. If not, summarize the current progress, and propose what is missing. : You must respond in the following format: : Status: (0 or 1. 0 for pending and 1 for finished) : Speak: (your words to the group if the task is pending, or a complete answer based on the full execution log to the user if the task is finished) : Now give your response. | 2308.10848#125 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 127 | Software Development with Group Setup &: An experienced programmer >: A software developer: A UI/UX designer¢%: A software tester Software Development with Solo Setup Draft Solution at Round O : @ Solver: : @e0o Simple Calculator Clear Color Difference Error Handle x) x] Functionality Keyboard Input Click Feedback [oe oe : QBS Reviewers: : Using âeval()° is unsafe : Use different colors to distinguish number and â Operation. Increase spacing between buttons. : It lacks a delete or backspace button Runnable Round O Output : @ Solver: ec0 Simple Calculator 7 4 1 0 Clear Delete Runnable Color Difference Error Handle Functionality Keyboard Input @ @ : Evaluator: â Completeness:8, Functionality: 8, : Readability: 7, Robustness: 7 : The keyboard input doesn't include : functionality for delete, clear, or calculate operations Round 1 : Draft Solution at Round1 : @ Solver: ec5o Simple Calculator Clear Delete Runnable Color Difference Error Handle Round 1 Output : @ Solver: ec0 Simple Calculator Clear Delete Runnable Color Difference Error Handle Click Feedback : Functionality Keyboard Input @ @ Click Feedback : : BEX Reviewers: : Does not handle the 'BackSpace' key | 2308.10848#127 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 128 | Delete Runnable Color Difference Error Handle Click Feedback : Functionality Keyboard Input @ @ Click Feedback : : BEX Reviewers: : Does not handle the 'BackSpace' key to allow : users to delete the last character : No click feedback : Change color Functionality Keyboard Input : Evaluator: : Completeness:9, Functionality: 9, : Readability: 9, Robustness: 9 :_ The code is well-structured, readable and : robust. It handles common exceptions and : provides clear feedback to [...] Click Feedback Round 0 @ Solver: eee Calculator 7 8 9 I 4 5 6 7 1 2 3 - i) = + Clear Runnable Color Difference Error Handle @ @ Functionality Keyboard Input Click Feedback @ (x) Evaluator: Completeness:8, Functionality: 7, Readability: 7, Robustness: 7 Use a safer way to evaluate mathematical expressions. Add more comments. Add more exception handling. Round 1 @ Solver: Calculator Clear Color Difference Error Handle @ @ Functionality Keyboard Input Click Feedback @ Runnable Evaluator: Completeness:9, Functionality: 9, Readability: 8, Robustness: 8 The code is well structured and accomplishes its task. There are comments that make | 2308.10848#128 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 130 | Round 0 @ Solver: eee Calculator 7 8 9 I 4 5 6 7 1 2 3 - i) = + Clear Runnable Color Difference Error Handle @ @ Functionality Keyboard Input Click Feedback @ (x) Evaluator: Completeness:8, Functionality: 7, Readability: 7, Robustness: 7 Use a safer way to evaluate mathematical expressions. Add more comments. Add more exception handling. Round 1 @ Solver: Calculator Clear Color Difference Error Handle @ @ Functionality Keyboard Input Click Feedback @ Runnable Evaluator: Completeness:9, Functionality: 9, Readability: 8, Robustness: 8 The code is well structured and accomplishes its task. There are comments that make it
Figure 12: An example of the process of software development.
29
Preprint | 2308.10848#130 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 131 | Figure 12: An example of the process of software development.
29
Preprint
import tkinter as tk from tkinter import mes root tk.Tk() root.title( ) input. = tk.StringVar() input_field = tk.Entry(ro =input_text) input_field.grid( =70) def button_click( yE current = input_text.get() input_text.set(current + str(item)) clear_all(): input_t: sset("") safe_evaluate( try: return str(eval( except Exception as return str(e) calculate(): try: for i in butto! button = tk.Button(root, i =Lambda i: button_click(item) if item else calculate()) button. grid( =row_val, col_val + if tk.Button(root, lambda: clear_all()) clear.grid( =5, root.mainloop()
Figure 13: The code generated by single agent.
30
!
Preprint | 2308.10848#131 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 132 | Figure 13: The code generated by single agent.
30
!
Preprint
tkinter tkinter Simple Calcu input_ tk.String entry tk.Entry(root, = input_text, ='readonly') r(item) ) input_t current [-1] [ety B= current = current[:-1] input_â entry vision by zero is not allowed") âred'}) essagel hi entry.config({ g button( t W n âwhite tk.Button(root, t li command, grid( =row, buttons â7, qe 4 button buttons: button ['y', '*', color = "light blue button: button_click(button) button button, command, row_val, l, col event. keysym == "BackSpace": delete()
# "Escapi
Figure 14: The code generated by the multi-agent group.
31
# Preprint | 2308.10848#132 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 133 | Expert Recruitment :1.⬠A chemical engineer : specialized in hydrogen : storage and fuel cell â technology. : 2. A civil engineer with : experience in the construction : of energy storage facilities. : 3.⬠An environmental : scientist with expertise in the Project Consulting with Group Setup in Horizontal Structure : BEX Discussion: :@®: Take into account regional weather conditions that may : affect the efficiency of the hydrogen storage system. Also, : consider the installation of leak detection systems at the : station. 1B: Site selection should consider proximity to the hydrogen : production source or infrastructure to minimize transportation : must be taken into account. We must also consider adding : safety measures such as pressure relief devices, leak detection Summarization 1.© emphasized on considering regional weather conditions [...] 2. suggested that site selection should be closer to the hydrogen production source [...] 3.⬠proposed to conduct an : impact and mitigation of : industrial projects. i systems and fire protection measures. ârisks associated with this storage station project. Iteration 0, Discussion Round 1 : QB Discussion : ®: While these are valuable inputs, | believe we also need to deliberate on advanced : materials for storage such as [...] | 2308.10848#133 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 134 | Discussion Round 1 : QB Discussion : ®: While these are valuable inputs, | believe we also need to deliberate on advanced : materials for storage such as [...] Moreover, evaluating the impact of high-pressure : storage on the material integrity of the tank is crucial to ensure safety and durability. We : should also consider the development of efficient fuel cells that can [...] : #: We should also consider the site soil properties for the stability of the storage tanks, : and perform a geotechnical survey to evaluate the risk of soil liquefaction and ground : settlement. Also, we need to take into consideration the construction of the foundation and : the structural design of the station to withstand high pressure and potential explosions. : >: We should also consider the installation of proper waste management systems to : handle any residual waste during the operation of the hydrogen storage station to protect both : the environment and local communities. Iteration 0, Discussion Round 2 : BE Discussion : ©: While the suggestions are generally good, the strategy could benefit from assessing the : potential for a cascading failure event due to [...] It is also crucial to develop contingency : plans for emergency situations and to continually monitor and maintain the storage : tanks to ensure their | 2308.10848#134 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 135 | event due to [...] It is also crucial to develop contingency : plans for emergency situations and to continually monitor and maintain the storage : tanks to ensure their optimal performance. Lastly, considering the use of liquid hydrogen : storage can be a beneficial option to enhance volumetric energy density. : ®: |t is essential to also consider the design and layout of the facility to ensure seamless : operation and maintenance, including [...] Also, the institute of seasonal-based structural : integrity checks ensures [...] Moreover, periodic safety drills and employee training : against possible hazards should be included in the plan. : >: The proposed plan can be enhanced by also considering the greenhouse gas : emissions during hydrogen production, the need for a comprehensive disaster management : plan, and strategies for ensuring biodiversity conservation during [...] Evaluation Evaluator â Comprehensiveness: 8 : Detailedness: 7 : Feasibility: 7 â Novelty: 6 i@. We should conduct a thorough environmental impact : assessment to identify potential ecological and human health environmental impact assessment to identify potential ecological and human health risks [...] Summarization 1. emphasized on considering regional weather conditions [...] | 2308.10848#135 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 136 | and human health environmental impact assessment to identify potential ecological and human health risks [...] Summarization 1. emphasized on considering regional weather conditions [...] They also suggested the use of advanced materials [...] 2. ® advised choosing a site closer to [...] They also pointed out the need to check site soil properties [...] 3.⬠proposed conducting [...] They also emphasized the need for proper waste management systems [...] Summarization 1. [...] Additionally, they pointed out the need to assess potential cascading failure events [...] 2. %[...] They also mentioned the importance of facility design and layout [...] 3. [...] They also mentioned the importance of considering greenhouse gas emissions during hydrogen production : Advice: More research is needed to understand the potential challenges and specific : ; requirements of using advanced materials such as metal hydrides and carbon nanotubes : â for hydrogen storage. The team should also consider the economic feasibility and cost- : effectiveness of the proposed solutions. To enhance the novelty of the project, the team â could explore emerging technologies or innovative approaches in hydrogen storage and â fuel cell development. For human resource staff, recruiting experts in advanced | 2308.10848#136 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 139 | Expert Recruitment : 1.4% A chemical engineer : specialized in advanced : materials and hydrogen : storage systems. : 2.4% An economist with : and cost-benefit analysis. : 3.@ A lawyer well-versed in â energy regulations and : environmental law in Ohio. | |} 434 Discussion : !B: While | agree with most points, | propose further research into : i the potential advantages and disadvantages of using : :advanced materials like [...] In addition, an evaluation of the : teconomic feasibility and cost-effectiveness of the proposed : expertise in energy economics : : material use and storage methods should be conducted before : : finalizing the solution. [...] : 1: [...] There are economic implications that need to be : : considered. This includes performing a detailed cost-benefit : :analysis to evaluate whether the benefits of the project outweigh : i the costs. [...] i: 3: [...] legal and regulatory compliance is also crucial. You need : : to consider the state and federal guidelines on hazardous Iteration 1, Discussion Round 0 Summarization 1.OL.] : 2. Bl...] 3...) ; 4. &% highlighted the need: for an evaluation of the: economic feasibility | 2308.10848#139 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 140 | Discussion Round 0 Summarization 1.OL.] : 2. Bl...] 3...) ; 4. &% highlighted the need: for an evaluation of the: economic feasibility [...] 5. &% suggested performing a detailed cost-benefit analysis [...] : 6. â¬Â¥emphasized the : need to comply with state : : ! material storage, [...] Iteration 1, Discussion Round 1 BP Discussion Be: | believe more thought should also go toward the regeneration and cycling life of metal : hydrides for hydrogen storage. [...] | suggest that we need to investigate low-temperature, : high-capacity hydrogen storage candidates which will combine efficiency and safety. [...] :& : The proposed steps seem comprehensive, however, they overlook certain economic : aspects that need to be considered. The cost-effectiveness analysis should also take into : account the global and local economic trends influencing energy sector investments, [...] : &: We should also consider including participation from local energy companies and : providers during the process of setting up the station. [...] Furthermore, we should also â consider obtaining a legal audit to ensure [...] Additionally, we may want to explore the legal : implications and financial feasibility of setting up a public-private partnership for this project, | 2308.10848#140 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 141 | legal audit to ensure [...] Additionally, we may want to explore the legal : implications and financial feasibility of setting up a public-private partnership for this project, : which might help address some of the funding concerns. Iteration 1, Discussion Round 2 HO Discussion 1B: [...] evaluating the potential of confinement in nanostructured materials to improve : hydrogen sorption properties is crucial. Moreover, the importance of optimizing the thermal : management system for the storage system cannot be overstressed. Lastly, we could also : explore the potential of multifunctional materials that could serve as both energy storage : and structural component in the station design. :®: [...] Also, it's crucial to account for the varying prices of hydrogen in the cost-benefit : analysis. Including cost forecasts for different scenarios in the economic model would provide : greater visibility into potential financial outcomes. [...] :@ : While the list is quite comprehensive, there are a few points | would like to add. We must : also be cognizant of Ohio's specific laws and regulations regarding the construction and : operation of new energy infrastructure, including those relating to water rights, noise : pollution, and air emissions from construction and operation, [...] | 2308.10848#141 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 142 | regarding the construction and : operation of new energy infrastructure, including those relating to water rights, noise : pollution, and air emissions from construction and operation, [...] Evaluation Evaluator Comprehensiveness: 9 : Detailedness: 8 â Feasibility: 8 : Novelty: 7 : Advice: and federal guidelines on : Summarization 1.O[..] 2. | 3. we] 4. & [...] suggested detailed examination of metal hydrides' regeneration and cycling life [...] 5. @ [...] advised considering global and local economic trends [...] 6. @[...] suggested involving local energy companies and providers [...] Summarization 1.00. 2. | 3. w] 4. & [...] noted the potential of the thermal management system optimization [...] 5. & [...] highlighted the volatility in the costs of advanced materials [...] 6. & [...] advised involving local energy companies, obtaining a legal : The ideas are comprehensive and well detailed, covering multiple aspects of the project, : : from engineering to environmental impact and legal matters. However, they lack a bit of â innovation, merely complying with existing standards. There's also a need for | 2308.10848#142 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 144 | Figure 15: (Page 2) An example process of project consulting with Group setup in horizontal decision- making structure. The agents are providing suggestions on the problem âGive me some suggestions if I want to build a compressed hydrogen storage station in Ohioâ
33
# Preprint
# Project Consulting with Solo setup
# Iteration O
# Discussion
: : : 1. : : 2. Advanced Compression Technology: Use state-of-the-art compression technology to maximize storage capacity and ensure the safe : containment of the hydrogen. : â
Based on the general task of generating ideas for a compressed hydrogen storage station in Ohio, | can suggest the following solution: Location Selection: Find an optimal location that complies with safety regulations and is close to major transportation routes. The site should also be in a remote location to minimize risk to populated areas.
3. Safety Measures: Implement strict safety measures, including pressure relief devices, leak detection systems, and automatic shutdown features. Regular safety audits should be conducted.
: 4. Green Energy Sources: To make the storage station more sustainable, consider using renewable energy sources, like solar or wind, to : power the compression process.
5. Collaboration with Universities and Research Institutions: Partner with local universities and research institutions to study and implement the most recent advances in hydrogen storage technology.
6. Community Engagement: Engage with the local community to educate them about the benefits of hydrogen energy and the safety measures in place at the storage station. | 2308.10848#144 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 145 | 6. Community Engagement: Engage with the local community to educate them about the benefits of hydrogen energy and the safety measures in place at the storage station.
: 7. Government Support: Seek support and funding from local and state government bodies who are interested in promoting clean energy : initiatives.
8. Infrastructure Development: Develop infrastructure for hydrogen fueling stations to promote the use of hydrogen-powered vehicles. Without specific critiques, it's difficult to tailor this solution to address any criticisms, but this comprehensive plan should offer a robust and forward-thinking approach to developing a compressed hydrogen storage station.
# Evaluation
# Evaluator
# : Comprehensiveness: 8 â Detailedness: 7 â Feasibility: 8 â Novelty: 7
: Advice: The ideas are comprehensive and practical, covering most aspects needed for the â project, including location, technology, safety, sustainability, collaborations, community â â involvement, government support, and infrastructure. However, more detailed implementation : â steps could be provided, such as specific technologies or strategies to be used, and : potential challenges to be addressed. The novelty is high, as it includes innovative suggestions â such as using renewable energy and partnering with universities. The brainstormers can try to â include [...]
# Iteration 1
# Discussion
: : : : : : : :
Based on the feedback received, here is an updated and detailed solution for the task of generating ideas for a compressed hydrogen storage station in Ohio: | 2308.10848#145 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 146 | # Discussion
: : : : : : : :
Based on the feedback received, here is an updated and detailed solution for the task of generating ideas for a compressed hydrogen storage station in Ohio:
1. Location Selection: Identify areas that are close to major transportation routes and in compliance with safety regulations. Use Geographic Information System (GIS) technology to create a detailed map of potential sites and analyze the potential impact on local ecosystems. 2. Advanced Compression Technology: Implement cutting-edge compression technology like lonic Liquid Piston Compressors or Advanced Adiabatic Compressors to maximize storage capacity and ensure safe containment. Regularly monitor and maintain these systems to prevent malfunctions.
:
: : : : : : :
: 3. Safety Measures: Install pressure relief devices, leak detection systems, and automatic shutdown features. Regular safety audits should â be conducted using both manual inspection and advanced sensor technology.
: 4. Green Energy Sources: Incorporate solar panels or wind turbines to power the compression process. Use energy storage systems to : store excess energy generated, ensuring consistent operation during periods of low renewable energy production.
: 5. : :
Collaboration with Universities and Research Institutions: Form research partnerships with institutions like Ohio State University or Case Western Reserve University. Involve them in the research and development of advanced hydrogen storage technologies and safety measures. | 2308.10848#146 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 147 | Community Engagement: Develop an information campaign using local media, town hall meetings, and school presentations to educate local community about the benefits and safety measures of hydrogen energy.
: 6. : the : : : : : : : â : : : :
7. Government Support: Apply for grants and incentives offered by the Ohio Development Services Agency, the Department of Energy, and other relevant bodies to support clean energy initiatives.
8. Infrastructure Development: Develop a network of hydrogen fueling stations, starting with areas that have a high concentration of hydrogen-powered vehicles. Collaborate with city planners to incorporate these stations into future urban development plans. Potential Challenges:
- Regulatory hurdles: Ensure all operations are in compliance with local, state, and federal regulations related to hydrogen storage and distribution.
- Cost: Develop a detailed financial plan and seek various funding sources to manage the high initial investment required for the project. - Public acceptance: If the public is not well informed about the benefits and safety measures of hydrogen energy, there could be opposition the A will address this issue.
# project.
# comprehensive community engagement plan
# help
By considering these detailed steps and potential challenges, we can develop a well-rounded and effective plan for a compressed hydrogen storage station in Ohio.
# to : 7 | 2308.10848#147 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 148 | # comprehensive community engagement plan
# help
By considering these detailed steps and potential challenges, we can develop a well-rounded and effective plan for a compressed hydrogen storage station in Ohio.
# to : 7
Evaluation Evaluator â Comprehensiveness: 9 â Detailedness: 8 : Feasibility: 7 â Novelty: 7 : Advice: The ideas are quite comprehensive and detailed, covering various aspects like site â selection, technology implementation, safety measures, usage of renewable energy, â collaboration with universities, community engagement, government support, and potential : â challenges. However, the feasibility can be improved by providing more detailed plans on how to : : overcome regulatory hurdles, manage costs, and gain public acceptance. The novelty is : : moderate as these ideas are not entirely new but aim to implement the best practices in the
Figure 16: An example process of project consulting in Solo setup. The agent is required to provide suggestions on the problem âGive me some suggestions if I want to build a compressed hydrogen storage station in Ohioâ.
34
Preprint | 2308.10848#148 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 149 | Project Consulting and Group setup in Vertical Structure Expert Recruitment Solver: @ ACEO with experience in managing a digital company : Reviewer: ! expertise in LLM agent platforms. ! 2. 4% A human resources : professional experienced in ! employee orientation and ! onboarding. 3. A project manager with a binpeneet ! background in tech startups. : @ Solver: ! Day 1: Company overview, mission, values, team } introductions, and culture immersion. ! Day 2: Detailed LLM platform training, including @ Solver: Day 1: Company overview, mission, and values. Introduction to AgentVerse's LLM platform. 11. A software engineer with â_ Day 2: Detailed training on LLM agent development. Day 3: Hands-on project to apply learned skills. ! Goal: Equip engineers with for effective contribution. Iteration 0, Discussion Round 1 Iteration 0, Discussion Round 0 BM Reviewers: ©: Include a session on troubleshooting : and debugging LLM agent platforms on Day 2 for a comprehensive understanding. ! #: Include team introductions and culture immersion on Day 1, and provide continuous support post-orientation for: better integration. >: Include a session on company culture and team building activities on Day 1 to | 2308.10848#149 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 150 | culture immersion on Day 1, and provide continuous support post-orientation for: better integration. >: Include a session on company culture and team building activities on Day 1 to foster a sense of belonging and knowledge and skills teamwork among new hires. | BM Reviewers: : Include a session on understanding the architecture and design principles of LLM agent platforms on Day 2. #:: Include a session on company policies, benefits, and expectations : on Day 1. Incorporate a mentorship program for ongoing support } troubleshooting and debugging. on Day 1. Incc : eyond Day ! Day 3: Hands-on project to apply learned skills. i : Post-orientation support provided. @: Include a session on company's development methodologies and : : tools on Day 2. Day 3 should also involve a mentor-guided project for better understanding | Iteration 0, Discussion Round 2 ! Goal: Equip engineers for effective contribution ! and foster teamwork. BY Reviewers: >: Include hands-on coding sessions on Day 2 for practical understanding of LLM platform and add a feedback session on Day 3 for continuous improvement. : @ Solver: ! Day 1: Company overview, policies, benefits, ! expectations, | 2308.10848#150 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 151 | and add a feedback session on Day 3 for continuous improvement. : @ Solver: ! Day 1: Company overview, policies, benefits, ! expectations, team introductions, and culture immersion. ! Day 2: LLM platform training, understanding architecture, ! design principles, and company's development ! methodologies. : Add a session on company's vision, mission, and values on: Day 1, and include hands-on coding session on Day 2 for practical ! exposure. : : Day 3: Mentor-guided project to apply learned skills. @: Add a session on Day 3 for feedback and Q&A to address any ! concerns or queries the new hires might have. : } Goal: Equip engineers for effective contribution, foster ! teamwork, and ensure ongoing mentorship support. | Iteration 0, Evaluation Evaluator : | Score: ! Comprehensiveness: 8 ! Detailedness: 7 Feasibility: 9 Novelty: 6 ! Day 1: Company overview, vision, mission, values, policies, ! benefits, team introductions, and culture immersion. : Day 2: LLM platform training, hands-on coding session, â understanding architecture, design principles, and development | methodologies. ! Advice: | 2308.10848#151 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 152 | : Day 2: LLM platform training, hands-on coding session, â understanding architecture, design principles, and development | methodologies. ! Advice: The proposal covers most aspects ! but lacks details on the specific content of the | ! Haining and the nature of the mentor-guided ! project. Consider adding more innovative ! Goal: Equip engineers for effective contribution, foster teamwork, : | elements to the orientation program. ! and ensure continuous improvement. ! Day 3: Mentor-guided project, feedback session, and Q&A. | 2308.10848#152 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 154 | Expert Recruitment Solver: & ACEO with experience ; âin managing a digital company : : Reviewer: : 1.43 A software engineer with Iteration 1, Discussion Round O & Solver: Day 1: Introduction to AgentVerse's ethos, policies, benefits, and team. Day 2: Detailed LLM platform training, PO Reviewers: : Include a session on company's vision, mission, and values on Day 1. Add hands-on coding session on Day 2 for practical exposure. Add a â expertise in LLM agent platform. session on Day 3 for feedback and Q&A to address any concerns or queries the new hires might have. @: [Agree] @3: [Agree] including coding exercises and Lo. @A human resources architecture exploration. : professional experienced in â employee orientation and training. Day 3: Mentor-led project focusing on real-world problem-solving, followed by 3.@9A project manager with a a feedback and Q&A session. : background in software : development projects. Goal: Enable engineers to contribute effectively, promote collaboration, and encourage innovation. Iteration 1, Discussion Round 1 & Solver: @4PS Reviewers: 3: Include a session on Day | 2308.10848#154 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 155 | effectively, promote collaboration, and encourage innovation. Iteration 1, Discussion Round 1 & Solver: @4PS Reviewers: 3: Include a session on Day 1 for understanding the basics of LLM 7 platform. On Day 2, focus on advanced features and troubleshooting. : Day 3 should include a real-world project, but also a review session for doubts and clarifications. Day 1: Introduction to AgentVerse's ethos, : policies, benefits, team, and a session on â company's vision, mission, and values. Day 2: Detailed LLM platform training, including : hands-on coding exercises and architecture @: Include a session on workplace culture and expectations on Day 1. : exploration. On Day 2, ensure the coding exercises are relevant to their roles. On : Day 3, provide a platform for new hires to share their learning : Day 3: Mentor-led project focusing on real-world experiences and insights. : problem-solving, followed by a comprehensive â feedback and Q&A session. @: Include a session on software development methodologies and : best practices. Also, consider a team-building activity to foster : Goal: Enable engineers to contribute effectively, | 2308.10848#155 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 156 | @: Include a session on software development methodologies and : best practices. Also, consider a team-building activity to foster : Goal: Enable engineers to contribute effectively, collaboration and team spirit among the new hires. â promote collaboration, and encourage innovation. Iteration 1, Discussion Round 2 : & Solver: HO Reviewers: #8: Include a session on company's vision, mission, and values on Day 1. Add hands-on coding session on Day 2 for practical exposure to LLM platform. On Day 3, incorporate a feedback and Q&A session to address concerns or queries. @: [Agree] 9: [Agree] : Day 1: Introduction to AgentVerse's ethos, policies, benefits, team, : workplace culture, expectations, and basics of LLM platform. Day 2: Advanced LLM platform training, role-specific coding exercises, : and session on software development methodologies. ' Day 3: Team-building activity, mentor-led project, review session for â doubts, and a platform for sharing experiences. Goal: Foster effective contribution, collaboration, and innovation. Iteration 1, Evaluation it Evaluator : | Score: : + Comprehensiveness: 8 : : | 2308.10848#156 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 157 | Foster effective contribution, collaboration, and innovation. Iteration 1, Evaluation it Evaluator : | Score: : + Comprehensiveness: 8 : : Detailedness: 8 : : Feasibility: 9 â Day 2: Advanced LLM platform training, hands-on coding session, Novelty: 7 â and software development methodologies. : ; Day 1: Introduction to AgentVerse's ethos, vision, mission, : values, policies, benefits, team, and workplace culture. : | Advice: The proposal covers most aspects : | but lacks detail on the content of the training : | and the nature of the team-building activity. It : : ! would be beneficial to include more specifics : : + about the LLM platform training and the : ! mentor-led project. Also, [...] Day 3: Team-building activity, mentor-led project, feedback and : Q&A session, and sharing experiences. ' Goal: Foster effective contribution, collaboration, and innovation. | 2308.10848#157 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 159 | Project Consulting with Solo Setup : Proposal: Initiate a 3-day orientation at AgentVerse for new ! engineers. : Day 1: Introduction to AgentVerse's mission and LLM platform. : Day 2: Hands-on training with LLM platform. ! Day 3: Team integration and project assignment. joal: Equip engineers with necessary knowledge and skills for ! effective contribution. ! Advice: The proposal should include more : details about the content of the training, the Score: : Comprehensiveness: 7 Detailedness: 6 ! Feasibility: 8 Novelty: 5 ! methods of team integration, and the criteria: for project assignment. Consider hiring : â experts in LLM platform and team building ! Proposal: A 3-day orientation for new engineers at AgentVerse. ! Day 1: Detailed overview of AgentVerse's mission and LLM : platform by experts. ! Day 2: Comprehensive hands-on LLM platform training. ! Day 3: Team integration via collaborative activities and project : assignment based on skills and interests. ! Goal: Effective knowledge transfer and team assimilation. Evaluator Score: Comprehensiveness: 7 : Detailedness: 6 Feasibility: 8 Novelty: 5 Advice: The proposal should include more details about the specific | 2308.10848#159 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 160 | Evaluator Score: Comprehensiveness: 7 : Detailedness: 6 Feasibility: 8 Novelty: 5 Advice: The proposal should include more details about the specific training activities and how the team integration will be : facilitated. Also, consider adding a feedback session for continuous improvement. ! Day 1: Introduction to AgentVerse's mission and LLM platform, including a Q&A session. ! Day 2: Hands-on LLM platform training with specific tasks and : problem-solving exercises. : Day 3: Team integration through collaborative projects, followed ! by a feedback session for improvement. : Goal: Knowledge transfer, team assimilation, and continuous : Novelty: 5 : Score: Comprehensiveness: 7 Detailedness: 6 Feasibility: 8 Advice: The proposal should include more : details about the specific tasks and i exercises, and consider adding a component : about the company culture and values. Also, : consider recruiting experts in LLM platform and team building for the orientation | 2308.10848#160 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 162 | & : Charlie ® Query: Recently, it has become popular to verify the mathematical reasoning abilities of LLMs by observing if they can solve the "24-Point Game." What is this game? Does it have a code-based solution? If it does, provide a Python code along with test cases and test its functionality. What are some other similar games that can be used to test the modelsâ mathematical reasoning abilities? Agents: pi: Bella Tools: b Bing Search API ivâ) Web Browser Code Interpreter Round 0 Round 1 : aâ â1. b: What is 24-point game?! 11. bb: Rule of 24-point game? ! 1. b: 24-point similar games? ! 12.6): Browse the 1st website | |2.4Q): Browse the Ist website! || !2.¢Q): Browse the Ist website | | B t '3.@@%J: Submit the rules ' 13.@: Write code + test cases! || '3.CQ: Browse the 2nd website! '2.â¬4: Submit the result \_----------------------- i } |] 14.69: "Make a Numberâ Rule? | | ' Lomo onan Seoeoesesoss Pa ; Evaluation Evaluation X Bella does | 2308.10848#162 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 163 | i } |] 14.69: "Make a Numberâ Rule? | | ' Lomo onan Seoeoesesoss Pa ; Evaluation Evaluation X Bella does not provide similar games ck(rules) 24-point game is ... (code) A Python code is written ... It (similar games) Similar games include âMake a Numberâ... J Round O Decision-Making BR [Bella]: Hi Charlie, let's first understand the tasks at hand. We need to: 1. Understand what is the â24-Point Game.â 2. Develop a Python code to solve it. 3. Test the code with some test cases. 4. Discuss similar games that test mathematical reasoning abilities. I suggest we divide the tasks as follows: - I can take up the first and last task. I'll find out what the 24-Point Game is and identify similar games that can be used to test mathematical reasoning abilities. I can use the âbing_searchâ tool for this. - As an experienced software engineer, you can handle the code development and testing. You can use the 'run_interpreter' tool to write and run the Python code. Does this sound good? & [Charlie] Hi Bella, your task assignment sounds good to me. I will be responsible for developing and testing the | 2308.10848#163 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 164 | write and run the Python code. Does this sound good? & [Charlie] Hi Bella, your task assignment sounds good to me. I will be responsible for developing and testing the Python code for the 24- Point Game. To clarify, I'll use the ârun_interpreterâ tool to code and test the solution. Let's get started. Round 1 Decision-Making eS [Bella]: Alright, let's wrap this up. I'll find similar games that can be used to test mathematical reasoning abilities. I'll use the âbing_searchâ tool for this task. Charlie, can you please share the Python code along with test cases for the "24-Point Game"? You can use the 'run_interpreterâ tool for executing and testing the Python code. & [Charlie] Sure Bella, I'll share the Python code and write more test cases for the "24-Point Game". I'll use the ârun_interpreterâ tool for this task. Let's complete our assigned tasks | 2308.10848#164 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 166 | Round 0 Round 1 Decision-Making Execution cision-Making Craft 1 bookshelf Collect Inventory at the End of this Round Inventory at the End of this Round 9 sugar canes & B@1@e ns | | M@aGeh) es M(@n@e sx | | M@nSehar ax : eit fn @een) (G2 Xn Gx ) ate! 3 leather x Collect 6 logs Round 3 Outcome + ng R Inventory at the End of this Round [email protected]«), a Bx Pd x11 3>¢ x1 x x1... 66% A @x2¥a Gx.) (A @ 2 Ka Kx.) Round 0 Decision-Making [Alice]: Hello Bob, Charlie. We have to craft a bookshelf. To do that, we will need 6 wooden planks and 3 books. To make 3 books, we'll need 3 leathers and 9 papers. To make 9 papers, E we'll need 9 sugar canes.. I suggest we split tasks like this: one of us will gather 6 wooden planks, one will gather 3 leathers, and one will gather 9 sugar canes. What do you guys think? 5 [Bob]: Alice, your plan sounds good. I can gather the 6 wooden planks. | 2308.10848#166 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 167 | one will gather 9 sugar canes. What do you guys think? 5 [Bob]: Alice, your plan sounds good. I can gather the 6 wooden planks. Charlie, are you okay with gathering the 3 leathers? [Charlie]: Sure, Bob. I can gather the 3 leathers. So, Alice, that leaves you with the task of gathering 9 sugar canes. Are you fine with this assignment? â ga 9 ig gal ign [Alice]: Yes, Charlie. I'm fine with gathering 9 sugar canes. So, our plan for this round is: Bob will gather 6 wooden planks, Charlie will gather 3 leathers, and I will gather 9 sugar canes. Round 1 Decision-Making [Bob]: Alice, Charlie, I have gathered the 6 wooden planks we need for the bookshelf. It seems both of you didn't manage to gather your materials. Let's try again. Alice, can you continue to find the sugar canes? And Charlie, can you keep trying to gather the leathers? We can do this, team! â â [Charlie]: Thanks for the encouragement, Bob. I will continue to try and gather the 3 leathers from the cows. Alice, are you okay with continuing to find | 2308.10848#167 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 168 | Thanks for the encouragement, Bob. I will continue to try and gather the 3 leathers from the cows. Alice, are you okay with continuing to find the 9 sugar canes? Let's keep ; working hard, we can complete this mission. ; = [Alice]: Yes, I will continue to look for the sugar canes. So, for this round, Bob, you can help me with gathering the sugar canes since you've already gathered the wooden planks. Charlie, } continue with gathering the leathers. Do we all agree? : â â IPP [Bob]: Alice, 1 agree with your plan. I'll join you in gathering the sugar canes. Charlie, are you fine with continuing to gather the leathers? Fa [Charlie]: Yes, Bob, I agree with that. I'll continue trying to gather the 3 leathers. Alice, you and Bob can focus on gathering the sugar canes. Let's keep pushing, team! Round 2 Decision-Making â = [Alice]: Great job, Bob! We have now gathered the sugar canes needed. Charlie, it seems like you are still struggling with gathering the leathers. Maybe Bob and I could help you with =} that. What do you think, Bob? Should we all focus on gathering | 2308.10848#168 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 169 | you are still struggling with gathering the leathers. Maybe Bob and I could help you with =} that. What do you think, Bob? Should we all focus on gathering the leathers? : [Bob]: Alice, I agree with your suggestion. Since we have already gathered the required sugar canes and wooden planks, our next focus should be on gathering the leathers. Charlie, we : will help you with this task. Let's all gather the leathers so we can progress with our mission. Are you okay with this plan, Charlie? : [Charlie]: Yes, Bob, I think that's a great idea. Gathering the leathers has proven to be more challenging than expected. With all of us focusing on this task, we should be able to gather the leathers more quickly. So, for this round, let's all focus on gathering the 3 leathers. Let's do this, team! Round 3 Decision-Making = [Alice]: Bob, good job on getting one leather. We still need two more. Charlie, I see you've been very busy crafting, but we need to focus on gathering the leathers. For this round, let's all continue to focus on getting the leathers. Are we in agreement? [Charlie]: Yes, Alice and Bob, I agree | 2308.10848#169 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 170 | the leathers. For this round, let's all continue to focus on getting the leathers. Are we in agreement? [Charlie]: Yes, Alice and Bob, I agree with the plan. I realize I got sidetracked with other tasks, and I will refocus on our main objective of gathering the remaining 2 leathers. âLet's continue with this mission, team! ' 5 [Bob]: Alice, I agree with your plan. We should all continue to focus on gathering the 2 remaining leathers. Charlie, are you okay with this assignment? Round 4 Decision-Making [Bob]: Well done team! We now have all the materials we need to craft the bookshelf. Alice, you have 3 leathers and 11 sugar canes, I have 1 leather and 11 sugar canes, and Charlie, « you have 2 leathers. Since we need 3 leathers and 9 sugar canes to make 3 books, Alice, you can craft the books. Charlie and I can give our leathers to you. After that, you can use the 6 wooden planks I have to craft the bookshelf. Does this sound good, Alice and Charlie? [Alice]: Bob, your plan sounds good. Alice, since you have most of the necessary materials already, it makes sense for you to craft the books and | 2308.10848#170 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10848 | 171 | Alice and Charlie? [Alice]: Bob, your plan sounds good. Alice, since you have most of the necessary materials already, it makes sense for you to craft the books and then the bookshelf. I will give my 2 leathers to you, Alice. Are you okay with this, Alice? [Bob]: Yes, Charlie, I am fine with crafting the books and the bookshelf. I will need both of your leathers to do that. So for this round, Charlie and Bob, please give your leathers to me. Then, I will craft the books and the bookshelf. | 2308.10848#171 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, inspired by human group dynamics, we propose a
multi-agent framework \framework that can collaboratively and dynamically
adjust its composition as a greater-than-the-sum-of-its-parts system. Our
experiments demonstrate that \framework framework can effectively deploy
multi-agent groups that outperform a single agent. Furthermore, we delve into
the emergence of social behaviors among individual agents within a group during
collaborative task accomplishment. In view of these behaviors, we discuss some
possible strategies to leverage positive ones and mitigate negative ones for
improving the collaborative potential of multi-agent groups. Our codes for
\framework will soon be released at
\url{https://github.com/OpenBMB/AgentVerse}. | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | [
{
"id": "2308.01862"
},
{
"id": "2307.16789"
}
] |
2308.10379 | 1 | # Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, and Ming Jin
Virginia Tech
# Abstract
Current literature, aiming to surpass the âChain-of-Thoughtâ approach, often resorts to an external modus operandi in- volving halting, modifying, and then resuming the genera- tion process to boost Large Language Modelsâ (LLMs) rea- soning capacities. This mode escalates the number of query requests, leading to increased costs, memory, and computa- tional overheads. Addressing this, we propose the Algorithm of Thoughtsâa novel strategy that propels LLMs through algorithmic reasoning pathways, pioneering a new mode of in-context learning. By employing algorithmic examples, we exploit the innate recurrence dynamics of LLMs, expand- ing their idea exploration with merely one or a few queries. Our technique outperforms earlier single-query methods and stands on par with a recent multi-query strategy that employs an extensive tree search algorithm. Intriguingly, our results suggest that instructing an LLM using an algorithm can lead to performance surpassing that of the algorithm itself, hinting at LLMâs inherent ability to weave its intuition into optimized searches. We probe into the underpinnings of our methodâs efficacy and its nuances in application. | 2308.10379#1 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
{
"id": "2204.02311"
},
{
"id": "2206.07682"
},
{
"id": "2305.10601"
},
{
"id": "2107.03374"
},
{
"id": "2301.13379"
},
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"id": "2108.07732"
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{
"id": "2305.04388"
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"id": "2211.09110"
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"id": "2201.08239"
},
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"id": "2302.07842"
}
] |
2308.10379 | 2 | reflect the introspective nature of System 2. Notably, inte- grating intermediary reasoning steps has yielded improve- ments in arithmetic reasoning tasks (Srivastava et al. 2022; Liang et al. 2022).
However, as tasks shift towards deeper planning and ex- tensive thought exploration, these methods appear restric- tive. Although CoT integrated with Self-Consistency (CoT- SC) (Wang et al. 2022) enlists multiple LLM outputs for a consensus, the lack of meticulous evaluation can result in model misdirection. The âTree of Thoughtsâ (Yao et al. 2023; Long 2023) emerges as a notable solution. While one LLM is dedicated to idea generation, another steps in to as- sess the merit of these ideas, following a halting-assessment- resuming cycle. This iterative process, anchored by tree search, has shown marked effectiveness, especially in tasks with a breadth of continuations. We see this progression as akin to humans employing tools to circumvent working memory limitations, serving as an external augmentation for LLMs (Mialon et al. 2023).
# Introduction | 2308.10379#2 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
{
"id": "2204.02311"
},
{
"id": "2206.07682"
},
{
"id": "2305.10601"
},
{
"id": "2107.03374"
},
{
"id": "2301.13379"
},
{
"id": "2108.07732"
},
{
"id": "2305.04388"
},
{
"id": "2211.09110"
},
{
"id": "2207.05221"
},
{
"id": "2212.10403"
},
{
"id": "2305.08291"
},
{
"id": "2307.13702"
},
{
"id": "2212.08073"
},
{
"id": "2210.09261"
},
{
"id": "2206.04615"
},
{
"id": "2305.05176"
},
{
"id": "2304.01852"
},
{
"id": "2201.08239"
},
{
"id": "2302.07842"
}
] |
2308.10379 | 3 | # Introduction
Recent developments in large language models (Chowdhery et al. 2022; Thoppilan et al. 2022; Liu et al. 2023, inter alia) have spotlighted their efficacy in general problem solving (Huang and Chang 2022; Suzgun et al. 2022), code gen- eration (Chen et al. 2021; Austin et al. 2021), and instruc- tion following (Ouyang et al. 2022; Bai et al. 2022). While early models relied on direct answer strategies (Brown et al. 2020), contemporary research veers towards linear reason- ing paths (Wei et al. 2022b; Kojima et al. 2022; Zhang et al. 2022) by breaking problems into sub-tasks for solution dis- covery, or harnesses external mechanisms to alter token gen- eration by changing the context (Zhou et al. 2022; Drozdov et al. 2022; Yao et al. 2023). | 2308.10379#3 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 4 | Analogous to human cognition (Sloman 1996; Kahneman 2011), early LLM strategies seemed to emulate the instan- taneous System 1, characterized by its impulsive decision- making. In contrast, more recent methodologies like chain- of-thought (CoT) (Wei et al. 2022b) and least-to-most prompting (L2M) (Zhou et al. 2022; Drozdov et al. 2022)
Preprint. Under review.
On the flip side, this enhanced LLM approach is not without pitfalls. A prominent downside is the substantial surge in the number of queries and computational demands. Each query to online LLM APIs such as GPT-4âa focal point of our studyâincurs a monetary expense (Chen, Za- haria, and Zou 2023) but also contributes to latency, a sig- nificant limitation especially critical in real-time applica- tions. Cumulative delays from these queries can compro- mise solution efficiency. Infrastructure-wise, continuous in- teractions can stress systems, leading to potential bandwidth constraints and reduced model availability (Aminabadi et al. 2022). Moreover, the environmental implications cannot be ignored; incessant querying escalates the energy consump- tion of already power-hungry data centers, exacerbating the carbon footprint (Wu et al. 2022; Dhar 2020). | 2308.10379#4 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 5 | With this in mind, our goal is to dramatically reduce the query counts employed by contemporary multi-query rea- soning methods while maintaining performance for tasks ne- cessitating adept use of world knowledge, thereby steering a more responsible and proficient use of AI resources.
Reflecting on the evolution of LLMs from System 1 to System 2, an essential ingredient comes to light: algorithms. Characterized by its methodical nature, the algorithmic per- spective offers a path to keenly explore problem spaces, en- act strategies, and formulate solutions (Helie and Pizlo 2022;
Standard Prompting
Algorithm of Thoughts | 2308.10379#5 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 6 | Model Input User: 8644 Assistant: Answer: (4 + (8 - 6) * 4 = 24 User. 111051 Model Output Assistant Answer: (11 - 1) * (10 - 5) = 24 Chain-of-Thought Model Input User: 8644 Assistant: 8-6=2 (left: 4, 4, 2) 4+2=6 (left: 6, 4) 6*4=24 Answer: (4+ (8 - 6)) * 4 = 24. User: 111051 Model Output Assistant: 11-10 = 1 (left: 1, 1, 5) 1*1=1 (left: 1,5) 5*1=5 (left: 5) Answer: ((11 - 10) * 1) *5 - 1 = 24. Model Input User: 8644 Assistant: 1. 4-4: (left: 8, 6, 0) 3.8 - 6: (left: 4, 4, 2) -4 +4: (left: 8, 2) 10, 6, 16, 4 - 4-4: (left: 2, 0) 2, 2, 0, undefined -4* 4: (left: 16, 2) 18, 14, 32, 8 -4/ 4: (left: 2, 1) 3, 1,2,2 - 4+ 2: (left: 6, 4) 10, 2, 24 | 2308.10379#6 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 8 | Figure 1: Comparison between standard prompting, CoT, and AoT in the game of 24. While standard prompting aims for a direct answer, CoT sketches out the successive steps to the final solution. AoTâs in-context example, distinct from CoT, integrates the search process, highlighted by markers â1â,..., â3â as âfirst operationsâ guiding subtree exploration for the problem set â8 6 4 4â. For clarity, only a single in-context example is displayed, with a focus on the third subtree exploration. AoT produces prospective search steps (e.g., the subtree exploration â5. 11 + 1â) and evaluates potential subsequent steps to either progress towards a solution or retrace to another viable subtree.
Banerjee et al. 2022). While much of the prevailing literature treats algorithms as external to LLMs, given LLMsâ inher- ent generative recurrence, can we channel this iterative logic to internalize an algorithm? | 2308.10379#8 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 9 | Drawing upon both the intricate nuances of human rea- soning and the disciplined precision of algorithmic method- ologies, our work aims to fuse these dual facets to aug- ment reasoning capabilities within LLMs. Existing research underscores that humans, when navigating complex prob- lems, instinctively draw upon past efforts, ensuring a com- prehensive contemplation rather than a narrow focus (Mon- sell 2003; Holyoak and Morrison 2005; Baddeley 2003). LLMs, with their generative span bounded only by token limits, appear poised to break through the barriers of human working memory. Spurred by this observation, we investi- gated if LLMs could mirror a similar layered exploration of ideas, referencing prior intermediate steps to sieve out infeasible options, all within their iterative generation cy- cle. And while humans excel with their intuitive acumen, al- gorithms stand out with organized, systematic exploration. Current techniques, like CoT, often sidestep this synergistic potential, imposing undue pressure on LLMs for on-the-spot precision. By capitalizing on LLMsâ recursive capabilities, we emulate a hybrid human-algorithmic approach. This is achieved through | 2308.10379#9 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 11 | More broadly, our approach signifies a new paradigm of in-context learning. Instead of the traditional âsupervised- learningâ mold of [PROBLEM, SOLUTION] or [PROBLEM, SUCCESSIVE STEPS TO SOLUTION], we present a new structure that covers [PROBLEM, SEARCH PROCESS, SO- LUTION]. Naturally, when instructing an LLM using an al- gorithm, the anticipation leans towards the LLM simply imitating the algorithmâs iterative thinking. However, what emerges as intriguing is the LLMâs ability to infuse its own âintuitionâ to achieve a search efficiency that even surpasses the algorithm itself (see Fig. 5).
In the subsequent sections, we first situate our work within the existing literature, followed by a discussion of our principal idea. We then present our experimental results and probe a series of hypotheses related to this emerging ca- pability of LLM before rounding off with a conclusion.
# Related Work | 2308.10379#11 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 12 | # Related Work
Standard Prompting. Also known as input-output prompting, it provides a few input-output examples of the task before getting an answer for the test sample from the language model (Brown et al. 2020). Although this method is very general and does not need any special prompting strategy, the performance is also worse compared to more advanced methods (Shao et al. 2023; Wei et al. 2022a; Lyu et al. 2023).
Standard Prompting Chain of Thoughts Tree of Thoughts Algorithm of Thoughts.
Figure 2: Illustration outlining various strategies for tackling reasoning problems with LLMs. Each box signifies a distinct thought, functioning as a unified string of words that forms an incremental pathway to reasoning. Green boxes indicate ideas deemed promising by the LLM, while red boxes represent less promising concepts. | 2308.10379#12 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 13 | Chain-of-Thought. In CoT, LLMs are presented with ex- amples where a given question x unfolds through a chain of intermediate reasoning pieces c1, . . . , cn to reach an an- swer y, represented as x â c1 â . . . â cn â y (Wei et al. 2022b; Lyu et al. 2023). By mimicking the examples in the context, the LLM automatically divides the solution into simpler linear steps to arrive at the answer, improv- ing performance across numerous reasoning benchmarks. Self-consistency (Wang et al. 2022) is a widely used de- coding strategy aimed at generating a variety of reason- ing paths by choosing the final answer through a majority vote, though this necessitates additional generations. Con- trary to CoTâs linear, direct progression, our approach pivots towards the explorative aspect of LLMs. We reconceptual- ize the c1, . . . , cn sequence, not merely as successive steps towards a solution, but as a dynamic, potentially mutable path that resembles an algorithmic search, allowing for ex- ploration, recalibration, and non-linear progression. | 2308.10379#13 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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] |
2308.10379 | 14 | Least-to-Most prompting (L2M). Taking cues from ed- ucational psychology (Libby et al. 2008), L2M prompting directs the LLM to decompose the central problem into smaller subproblems. Each subproblem is tackled in se- quence, with the outcome appended before progressing to the next (Zhou et al. 2022; Drozdov et al. 2022). While this structured delineation is beneficial for broader generaliza- tion, it operates on the premise of finding a nearly perfect de- composition in a single attemptâideal for problems with a clear-cut structure. Yet, when tasks intertwine with their de- composition complexities (like games of 24), this methodâs inflexibility becomes apparent. Contrastingly, AoT not only underscores the active subproblem (as shown in Fig. 1), but also permits a more contemplative approach by entertaining various options for each subproblem, while maintaining ef- ficacy even with minimal prompts.
2023). Evaluation capabilities of LLMs can also be used to direct the search by pruning nodes that are hopeless to in- crease efficiency. However, ToTâs Achillesâ heel is its ex- cessive reliance on LLM queries, at times necessitating hun- dreds for just one problem. We tackle this limitation by gen- erating the whole thought process within a single context.
# Algorithm of Thoughts | 2308.10379#14 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 15 | Our strategy pivots on recognizing a core shortcoming of current in-context learning paradigms. CoT, while enhanc- ing the coherency of thought linkages leading to solutions, occasionally falters, presenting incorrect intermediate steps (Zelikman et al. 2022; Turpin et al. 2023; Lanham et al. 2023). Faithful CoT (Lyu et al. 2023) ought to amend this by eliciting symbolic chains of reasoning where the LLMâs output resembles task-specific pseudo-code, primed for de- terministic execution like Python. The intention is only to use the thought processes but not the outputs and inputs of each link since they have a tendency to be unreliable. But, the occasional missteps of CoT may not necessarily due to the LLMâs inability to compute correctly. The LLM, when confronted with questions that closely match conditions of previous in-context examples, may favor echoing those out- puts over generating the appropriate questions. To shed light on this phenomenon, we designed an experiment. Querying text-davinci-003 for arithmetic tasks (e.g., â11 â 2 =â), we | 2308.10379#15 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 16 | phenomenon, we designed an experiment. Querying text-davinci-003 for arithmetic tasks (e.g., â11 â 2 =â), we prefixed them with multiple in-context equations converging to an identical output (e.g. â15 â 5 = 10, 8 + 2 = 10â). Our results, presented in Fig. 3, reveal a steep decline in accu- racy, suggesting that the mere presence of correct reasoning in the context might inadvertently compromise even basic arithmetic skills. | 2308.10379#16 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 17 | Tree of Thoughts (ToT). In the cases where each sub- problem has multiple viable options to explore, linear rea- soning paths from CoT or L2M substantially limit the cov- erage of the thought space. Considering possible options for each subproblem, the decision tree can be explored by ex- ternal tree-search mechanisms (e.g., BFS, DFS) (Yao et al.
To offset this bias, diversifying the outputs of examples might seem like a viable solution, but this could subtly skew the distribution of outputs. Merely adding unsuccessful tri- als, much like a random search, might inadvertently encour- age the model to retry rather than truly solve. Capturing the true essence of algorithmic behavior, where both failed searches and subsequent recovering and learning from such attempts play a role, we incorporate in-context examples pat1.0 04 Probability of Correct Token 0 2 4 6 8 vo a2 # of Equations
Figure 3: The probability of generating the correct token as we add more in-context examples that are correct but possess identical outputs.
terned after search algorithms, notably depth-first search (DFS) and breadth-first search (BFS). See Fig. 1 for an ex- ample. | 2308.10379#17 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 18 | terned after search algorithms, notably depth-first search (DFS) and breadth-first search (BFS). See Fig. 1 for an ex- ample.
This paper focuses on a broad class of tasks reminiscent of tree-search problems. These tasks necessitate breaking down the main problem, crafting feasible solutions for each seg- ment, and making decisions on the paths to either pursue or forsake, with the option of reevaluating more promising segmentations. Rather than posing separate queries for ev- ery subset, we leverage the iterative capabilities of the LLM to address them in one unified generation sweep. By confin- ing ourselves to one or two LLM interactions, this approach naturally incorporates insights from antecedent context can- didates and tackles intricate issues requiring an in-depth ex- ploration of the solution domain. In alignment with our goal, we also give insights into how small or big those thoughts should be and what type of in-context examples should be given to the LLM to promote token efficiency. Subsequently, we outline key components of tree-search algorithms and their manifestation in our framework. | 2308.10379#18 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 19 | 1. Decomposition into Subproblems. Given a problem, constructing a search tree that delineates feasible reasoning pathways is already a demanding task, excluding the actual problem-solving aspect. Any decomposition must consider not just the interrelations between subtasks, but also the ease of addressing each individually. Consider a simple multi- digit addition: while converting numbers to binary might be efficient for a computer, humans typically find base 10 arithmetic more intuitive. Furthermore, even if the subprob- lems remain constant, their execution might vary. Intuition can lead to shortcuts between solution steps, while its ab- sence might necessitate more detailed steps. Crafting the right prompt (i.e., in-context algorithmic examples) hinges on these nuances, determining the minimal tokens an LLM would need for dependable performance. This is not only essential to fit within the LLMâs context constraints but also vital for efficacy, as weâd expect LLMs to address problems resonant with its context using a similar token volume.
2. Proposing Solutions to Subproblems. A dominant ap- proach in existing works involves direct sampling from LLM token output probabilities (Wang et al. 2022; Yao
Text Completion The first five prime numbers: 2 = 87.6% 2 on/p 1=12.3% probabilities for the first token | 2308.10379#19 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 20 | Text Completion The first five prime numbers: 2 = 87.6% 2 on/p 1=12.3% probabilities for the first token
Figure 4: An example highlighting the drawback of isolated sampling of sequenced ideas. Input is denoted in blue, with the text-davinci-003 providing the green completions.
et al. 2023). Though effective for one-off answers (Kadavath et al. 2022) (with certain constraints (Robinson and Wingate 2022)), this method falls short in scenarios demanding a se- quence of samples to be integrated or evaluated within sub- sequent prompts (Robinson and Wingate 2022). To mini- mize model queries, we adopt an uninterrupted solution cre- ation process. Here, we directly and continuously generate solutions for the prevailing subproblem without any genera- tion pauses. | 2308.10379#20 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 21 | The benefits are three-fold. First, with all generated solu- tions existing within a shared context, thereâs no need for in- dividual model queries for each solution evaluation. Second, while it may seem counterintuitive initially, isolated token or token group probabilities might not always yield meaning- ful choices. A simple illustration is found in Fig. 4. When evaluated independently, the second-most probable token for our inaugural number is â1âânot qualifying as prime. But, when generation remains unbroken, the derived sequence is correct. This incongruence points towards the restrictive na- ture of the Markov property in sequence modeling. Core to our perspective is the premise that for sequential tasks like algorithmic search, LLMs are more adept at generating en- tire sequences than intermittently pausing and re-initiating the token sampling process. | 2308.10379#21 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 22 | 3. Gauging the Promise of a Subproblem. As above, existing techniques lean on additional prompting to dis- cern the potential of tree nodes, aiding decisions regard- ing exploration direction. Our observations suggest that if the most promising routes are encapsulated within the in- context examples, LLMs inherently gravitate towards prior- itizing those promising candidates. This diminishes the need for intricate prompt engineering and allows the incorpora- tion of intricate heuristics, whether intuitive or knowledge- driven. Again, the absence of disjoint prompts in our ap- proach allows for an immediate assessment of candidate vi- ability in the same generation.
4. Backtracking to a Preferable Juncture. The decision of which node to explore next (including retracing to a prior node) inherently depends on the selected tree-search algo- rithm. While previous studies (Yao et al. 2023) have em- ployed external means such as coded mechanisms for the search process, this restricts its broader appeal and entails additional customization. Our designs predominantly adopt a DFS approach supplemented by pruning. The aim is to | 2308.10379#22 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 23 | maintain proximity between nodes sharing the same par- ent, thereby encouraging the LLM to prioritize local over distant features. Additionally, we present performance met- rics for the AoT approach grounded in BFS. Our reliance on the modelâs inherent capacity to glean insights from in- context examples obviates the necessity for additional, be- spoke mechanisms.
Experiments We show that AoT surpasses the performance of other single-prompt methods (e.g. standard, CoT/-SC prompting) while remaining competitive even when compared to meth- ods that utilize external mechanisms, such as ToT, in bench- marks like the game of 24 and 5x5 mini crosswords.
Game of 24 The game of 24 is a mathematical card game in which play- ers are given four numbers and must use addition, subtrac- tion, multiplication, and division (each operation can be used more than once) to manipulate those numbers to total 24. For instance, for the numbers â8 8 5 4â, one solution would be â8 â (5 â (8/4)) = 24â. At first glance, the game might appear straightforward. However, a cursory calculation sug- gests there are nearly 13,000 distinct expressions possible for any set of four numbers (without accounting for the com- mutative properties of addition and multiplication), making it a formidable challenge for present-day LLMs. | 2308.10379#23 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 24 | Task Setup. Adhering to the setup detailed in (Yao et al. 2023), we use games from indices 901-1000, sourced from the 1362 games ranked by relative difficulty at 4nums.com. For an attempt to be considered successful, it must derive a total of 24 using the exact numbers provided and only the allowed operations.
Baselines. Standard prompting and CoT are used in the 5- shot setting, with CoT integrating 3 steps for the operations. These methods are sampled 100 times, and the averaged suc- cess rates from these samples are reported. CoT-SC is also tested with 100 votes in our setup. For ToT, we use a breadth of 5. The performance metrics from their study are directly cited to obviate the need for needless carbon emissions. | 2308.10379#24 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 25 | AoT Setup. We employ the same 5-shot setting as in stan- dard prompting and CoT baseline setup. Our in-context sam- ples leverage a DFS-style search algorithm, which, for clar- ity, is the same version used when contrasting with tra- ditional DFS in Fig. 5. During each subtree exploration, dubbed either the âfirst stepâ or âfirst operationâ, we choose two numbersâillustrated by the selection of 8 and 6 in the third âfirst stepâ (i.e., subtree labeled â3â) of Fig. 1âand a corresponding operation (e.g., 8 â 6). This operation results in a new number, 2, leaving us with three numbers in total. A thorough combing of these three numbers culminates in 19 leaf nodes, all visible under the â3â subtree in Fig. 1. We aim to assess two aspects: the ability of the LLM to pin- point promising first operations, which directly impacts the number of resolved leaf nodes, and its performance against a conventional DFS. Details on the prompts we employed are provided in the Appendix. As our method emphasizes
sequential generation over trajectory sampling, we operate with a temperature setting of 0. | 2308.10379#25 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 26 | sequential generation over trajectory sampling, we operate with a temperature setting of 0.
Results. From Table 1, itâs evident that standard prompt- ing combined with CoT/-SC significantly lags behind tree search methods when used with LLMs. The âStandard + Re- fineâ result, showing a 27% success rate, is referenced from (Yao et al. 2023). This method involves iteratively asking the LLM (up to 10 iterations) to refine its answer if the initial one is incorrect. Meanwhile, ToT is limited to a maximum of 100 node visits, translating to several hundred LLM queries for each example. Remarkably, AoT achieves its results with just a single query. Despite reducing the number of requests by more than a factor of 100, AoT still outperforms ToT in this task.
Method Standard Prompting CoT CoT-SC (k = 100) Standard + Refine ToT (b = 5) AoT (ours) Success Avg. Queries 7.3% 1 4.0% 1 100 9.0% 10 27% 109.1 69% 1 71%
Table 1: Game of 24: success rates and the average number of LLM queries for each example. | 2308.10379#26 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 27 | Error Analysis. Using a strictly LLM-centric approachâ eschewing any external tooling or editsâwe sought to cat- egorize mistakes observed during the game of 24. This aids in highlighting areas for refinement when solely deploying LLMs. Weâve classified these errors into four distinct, ex- haustive categories: 1) Out-of-token error: The LLM reaches its maximum token threshold without identifying a solution. 2) Expression misstep: The LLM has the correct logic or steps but fails when trying to express or formulate them into a coherent answer. 3) Non-finalization error: The LLM dis- covers the solution but continues its search without consol- idating the finding. 4) Other errors: This umbrella term en- compasses other mistakes like computational errors that re- sult in overlooking the solution or furnishing incorrect an- swers. To exclusively showcase the AoTâs search capabil- ities, we also present the AoT + Manual Resolution ver- sion. Here, once the LLM pinpoints a solution, its final ar- ticulation is manually processedâa strategy also employed by the ToT method. As evidenced in Table 2, a | 2308.10379#27 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 28 | LLM pinpoints a solution, its final ar- ticulation is manually processedâa strategy also employed by the ToT method. As evidenced in Table 2, a notable 7% of mistakes stem from non-algorithmic factors like non- finalization and expression missteps. In fact, with manual resolution, AoT attains a 78% success rate, surpassing ToT. This underlines the potential for refining our prompt, espe- cially in areas concerning recognizing and expressing suc- cessful problem resolutions. Additionally, the token limi- tation underscores the appeal of expanding the generative context window, which may further bolster LLMsâ recursive reasoning when engaged with algorithmic examples. | 2308.10379#28 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 29 | Error Type Out-of-token error Expression misstep Non-finalization error Others Method ToT AoT AoT + Manual Resolution Error 9% 4% 3% 13% Success 69% 71% 78%
Table 2: Game of 24: AoT error analysis.
Mini Crosswords The 5 Ã 5 mini crossword is a compact word puzzle featur- ing a grid of 25 squares arranged in a 5-by-5 configuration. Players are tasked with filling the grid based on provided clues for each word. Clues are given for words that run both across (horizontally) and down (vertically). Words intersect at certain letters, offering additional hints to complete the puzzle.
Task Setup. Adhering to the setup outlined in (Yao et al. 2023), we draw our prompts from games 136, 141, 146, 151, and 156 out of the 156 games available on goobix.com. Our testing focuses on a set of 20 games, specifically games 1, 6, . . ., 91, and 96. | 2308.10379#29 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
{
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2308.10379 | 30 | Baselines. Mirroring our approach for the game of 24, we benchmark our method against established techniques: stan- dard prompting, CoT, and ToT. For standard prompting, we provide both the crosswords and their respective solutions as in-context examples. CoT augments this by prompting the retrieval of words for each of the ten cluesâequally split between horizontal and vertical orientations. We directly ex- tract the success rates of ToT from their original publication for comparison. | 2308.10379#30 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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2308.10379 | 31 | AoT Setup. We divide the process into two steps, each in- volving a query. Initially, we task the LLM with suggesting five potential words for each row and column. We then pin- point the starting word candidates that have the highest com- patibility with other words within the crossword framework. This preliminary phase mirrors a âwarm-upâ sequence in al- gorithm initialization. In the subsequent step, we exclusively leverage the LLMâs algorithmic reasoning prowess, starting with the pre-selected word. The method involves cyclically choosing a likely option (specifically, a row or column) for insertion, generating candidate words, and assessing their compatibility with the words already on the board. If no match is found, the process shifts focus to another promising candidate. Otherwise, the word is added to the crossword, and the search continues. The cycle concludes either when the board is fully populated or no more suitable words can be found, which may be due to either incorrect existing words or the absence of matching words. Notably, this entire pro- cess unfolds within a single generation window. The algo- rithmic examples in our prompt (detailed in the Appendix)
include three that achieve game completion and two that pre- dominantly populate the crossword, filling 8 or 9 slots. | 2308.10379#31 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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] |
2308.10379 | 32 | include three that achieve game completion and two that pre- dominantly populate the crossword, filling 8 or 9 slots.
Results. Table 3 underscores AoTâs proficiency in the mini crosswords task, showcasing a word success rateâa measure used in existing studies to represent the percent- age of words correctly completed out of the totalâthat sur- passes earlier methods reliant on various prompting tech- niques. However, it trails behind ToT. An important observa- tion is the sheer volume of queries ToT employs, exceeding AoTâs by over a factor of 100. One factor hindering AoT from surpassing ToT is that the backtracking capability in- herent in the algorithmic example isnât fully activated. Fully unlocking this capability would lead to a significant elonga- tion in the generation phase. In contrast, ToT has the advan- tage of leveraging external memory for its backtracking.
Method Standard Prompting CoT ToT AoT (ours) Word Success Avg. Queries 14% 15.6% 60% 52% 1 1 >200 2
Table 3: 5 Ã 5 mini crosswords word: word success rates and the average number of LLM queries for each example. | 2308.10379#32 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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}
] |
2308.10379 | 33 | Table 3: 5 Ã 5 mini crosswords word: word success rates and the average number of LLM queries for each example.
Error Analysis. To understand the prevalent mistakes made by AoT, weâve categorized the errors into four dis- tinct categories. In our analysis for each game, we focus on the initial error the LLM produces while charting its rea- soning path, given that an early error typically cascades into subsequent failures. 1) No preselections: LLM fails to gen- erate compatible words essential for the warm-start phase. Given a correctly preselected word, the second phase for re- cursive reasoning can exhibit errors including: 2) Expres- sion misstep: The LLM mistakenly believes it has exhausted all choices and jumps to an answer prematurely. 3) Incor- rect pattern extraction: The LLM wrongly extracts a pattern based on the current board layout. 4) Erroneous word place- ment: Despite recognizing the correct pattern, the LLM se- lects a mismatched word or misses better-fitting alternatives. Navigating the crossword complexity arises from outdated terms, esoteric references, and typographical mishaps. Pre- dominantly, the errors observed are due to misguided word placements followed by pattern misinterpretations. Also, the LLM seems challenged in aligning letters at precise indices to create word structuresâ an obstracle circumvented by an external mechanism in the ToT framework. | 2308.10379#33 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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},
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"id": "2206.04615"
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"id": "2305.05176"
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"id": "2304.01852"
},
{
"id": "2201.08239"
},
{
"id": "2302.07842"
}
] |
2308.10379 | 34 | Discussion In this section, we delve into crucial aspects to consider when crafting prompts for AoT, using the game of 24 as our primary case study.
Can AoT surpass the DFS itâs patterned after? A core query of ours is to ascertain if the LLM has the capability to not only mirror but also outdo the efficiency of the al- gorithm introduced in-context. As evidenced in Fig. 5, AoT
Error Type No preselections Expression misstep Incorrect pattern extraction Erroneous word placement Error 15.8% 5.3% 26.3% 52.6%
Table 4: Breakdown of errors in 5 Ã 5 mini crosswords with AoT. Numbers indicate the relative percentage of each error type among all errors.
systematically navigates fewer nodes than its DFS counter- part. While DFS employs a uniform strategy when choosing the subsequent subtree to investigate, AoTâs LLM integrates its inherent heuristic. This amplification over the base algo- rithm exemplifies the advantages of LLMâs recursive reason- ing capability.
20 # of Games DFS AoT 0 200 400 600 800 1000 # of Visited Nodes
Figure 5: Histogram showing the number of visited nodes for AoT and DFS in the Game of 24. | 2308.10379#34 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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},
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{
"id": "2201.08239"
},
{
"id": "2302.07842"
}
] |
2308.10379 | 35 | Figure 5: Histogram showing the number of visited nodes for AoT and DFS in the Game of 24.
How does algorithm selection influence AoTâs efficacy? To explore the impact of algorithm choice on AoTâs per- formance, we implemented both BFS and random search within the AoT framework. Our findings, presented in Ta- ble 5, reveal that all three AoT variations outperform the single-query CoT. This outcome was anticipated as AoT, ir- respective of the algorithm, undertakes a search and revis- its potential mistakesâeither by random retry in the ran- dom search variant or through backtracking in the DFS and BFS configurations. Notably, the structured search versions, AoT (DFS) and AoT (BFS), displayed better efficiency than AoT (Random), underscoring the advantage of algorithmic insights in solution discovery. However, AoT (BFS) lagged behind AoT (DFS). Closer inspection of errors made by AoT (BFS) revealed the LLM faced greater challenges in identi- fying optimal operations than its DFS counterpart. | 2308.10379#35 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased costs, memory, and computational overheads. Addressing this, we
propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
algorithmic reasoning pathways, pioneering a new mode of in-context learning.
By employing algorithmic examples, we exploit the innate recurrence dynamics of
LLMs, expanding their idea exploration with merely one or a few queries. Our
technique outperforms earlier single-query methods and stands on par with a
recent multi-query strategy that employs an extensive tree search algorithm.
Intriguingly, our results suggest that instructing an LLM using an algorithm
can lead to performance surpassing that of the algorithm itself, hinting at
LLM's inherent ability to weave its intuition into optimized searches. We probe
into the underpinnings of our method's efficacy and its nuances in application. | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | [
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}
] |
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