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| references
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2306.05087
| 22 |
6
Table 1: Comparative analysis of evaluation results from various annotation models. The tuple in the table means (#win,#lose,#tie). Specifically, (72,28,11) in the first line of the table indicates that LLaMA-7B outperforms Bloom-7B in 72 responses, underperforms in 28, and matches the quality in 11 responses. The âJudged Byâ column represents different methods of response evaluation. âHumanâ indicates that humans evaluate the result, and âPandaLMâ indicates that our proposed PandaLM model evaluates the result.
|
2306.05087#22
|
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
|
Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishing such a benchmark is not a trivial task due to the challenges
associated with evaluation accuracy and privacy protection. In response to
these challenges, we introduce a judge large language model, named PandaLM,
which is trained to distinguish the superior model given several LLMs.
PandaLM's focus extends beyond just the objective correctness of responses,
which is the main focus of traditional evaluation datasets. It addresses vital
subjective factors such as relative conciseness, clarity, adherence to
instructions, comprehensiveness, and formality. To ensure the reliability of
PandaLM, we collect a diverse human-annotated test dataset, where all contexts
are generated by humans and labels are aligned with human preferences. Our
results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation
ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM
enables the evaluation of LLM to be fairer but with less cost, evidenced by
significant improvements achieved by models tuned through PandaLM compared to
their counterparts trained with default Alpaca's hyperparameters. In addition,
PandaLM does not depend on API-based evaluations, thus avoiding potential data
leakage. All resources of PandaLM are released at
https://github.com/WeOpenML/PandaLM.
|
http://arxiv.org/pdf/2306.05087
|
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang
|
cs.CL, cs.AI
| null | null |
cs.CL
|
20230608
|
20230608
|
[
{
"id": "2302.13971"
},
{
"id": "2204.02311"
},
{
"id": "1803.05457"
},
{
"id": "2305.10403"
},
{
"id": "1807.05118"
},
{
"id": "2211.05100"
},
{
"id": "2302.10198"
},
{
"id": "2205.01068"
},
{
"id": "2003.05689"
},
{
"id": "1806.03822"
},
{
"id": "1711.05101"
},
{
"id": "2304.03208"
},
{
"id": "2304.01373"
},
{
"id": "2303.14742"
},
{
"id": "2303.04673"
},
{
"id": "2212.10560"
},
{
"id": "2211.08073"
},
{
"id": "2210.02414"
},
{
"id": "2304.03277"
},
{
"id": "2002.06305"
},
{
"id": "2305.13412"
},
{
"id": "2304.01196"
}
] |
2306.05152
| 22 |
At a greater level of autonomy (Conversational Testing with Tools or higher), we argue that one of the greatest beneï¬ts LLMs can bring about is the fact that they can codify and im- plement non-formalized testing scripts that are still manually processed [3], [25] based on their natural language processing capabilities. For example, we can imagine a conversational testing agent interpreting and executing natural language test- ing guidelines written for humans, executing tools and seeking clariï¬cations if needed via the middleware. As such non- formalized testing techniques or guidelines are intended for humans, they could be readily adopted as part of already- existing testing practices, which can improve developer ac- ceptance of results [26], while also acting as explanations for any generated results [27]. At the greatest level of autonomy, LLMs would formulate and execute testing plans, while con- versing with the developer on abstract terms. For example, in our example from the previous section, a human had to copy-and-paste the generated tests from an LLM and manually execute the tests; with the appropriate middleware, this process could be automated, and the developer would only need make higher-level decisions. As a result, this level of autonomy
|
2306.05152#22
|
Towards Autonomous Testing Agents via Conversational Large Language Models
|
Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. Recent discoveries of the capabilities of large language models
(LLMs) suggest that they can be used as automated testing assistants, and thus
provide helpful information and even drive the testing process. To highlight
the potential of this technology, we present a taxonomy of LLM-based testing
agents based on their level of autonomy, and describe how a greater level of
autonomy can benefit developers in practice. An example use of LLMs as a
testing assistant is provided to demonstrate how a conversational framework for
testing can help developers. This also highlights how the often criticized
hallucination of LLMs can be beneficial for testing. We identify other tangible
benefits that LLM-driven testing agents can bestow, and also discuss potential
limitations.
|
http://arxiv.org/pdf/2306.05152
|
Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo
|
cs.SE
| null | null |
cs.SE
|
20230608
|
20230905
|
[
{
"id": "2305.10601"
},
{
"id": "2303.17580"
},
{
"id": "2305.16291"
},
{
"id": "2201.09305"
},
{
"id": "2210.03629"
},
{
"id": "2211.10435"
},
{
"id": "2303.12712"
},
{
"id": "2302.03287"
},
{
"id": "2209.11515"
}
] |
2306.05171
| 22 |
Input: A high level instruction i; state information s; manager, object that can generate a base task list based on its map, a set of valid general operations(each can be generated by a planner) and their description(about what they can do, the meaning of their parameters, some possible subtask); planners, a set of objects that can generate different task, and they each have its own map, a set of valid operations and their description(about what they can do and the meaning of their parameters); allocator, an object who can allocate robots to specific node/leaf in the task tree based on i and s. Output: A tree whose leaf represents a function which the robot can execute
4. The Allocator assigns the operations to be executed by the actual machines based on the task tree and task description, and assigns specific machines to the nodes of the task tree.
Table. 2. Explanation of the three types of entities required in the process of decoupling and coordination.
Entities Design Manager â Obtains the task description and generates a flat sequence of overall task words, then invokes the entity to generate the task tree for each overall task word. Planner â Generates the task tree for a specific overall task word. Allocator â Allocates tasks to specific machines based on the task description and the forest.
|
2306.05171#22
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05212
| 22 |
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. CoRR, abs/2302.13971.
Liang Wang, Nan Yang, and Furu Wei. 2023. Query2doc: Query expansion with large language models. CoRR, abs/2303.07678.
Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, et al. 2022. Glm-130b: An open bilingual pre-trained model. arXiv preprint arXiv:2210.02414.
Jingtao Zhan, Qingyao Ai, Yiqun Liu, Jiaxin Mao, Xi- aohui Xie, Min Zhang, and Shaoping Ma. 2022. Disentangled modeling of domain and relevance arXiv preprint for adaptable dense retrieval. arXiv:2208.05753.
|
2306.05212#22
|
RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
|
Although Large Language Models (LLMs) have demonstrated extraordinary
capabilities in many domains, they still have a tendency to hallucinate and
generate fictitious responses to user requests. This problem can be alleviated
by augmenting LLMs with information retrieval (IR) systems (also known as
retrieval-augmented LLMs). Applying this strategy, LLMs can generate more
factual texts in response to user input according to the relevant content
retrieved by IR systems from external corpora as references. In addition, by
incorporating external knowledge, retrieval-augmented LLMs can answer in-domain
questions that cannot be answered by solely relying on the world knowledge
stored in parameters. To support research in this area and facilitate the
development of retrieval-augmented LLM systems, we develop RETA-LLM, a
{RET}reival-{A}ugmented LLM toolkit. In RETA-LLM, we create a complete pipeline
to help researchers and users build their customized in-domain LLM-based
systems. Compared with previous retrieval-augmented LLM systems, RETA-LLM
provides more plug-and-play modules to support better interaction between IR
systems and LLMs, including {request rewriting, document retrieval, passage
extraction, answer generation, and fact checking} modules. Our toolkit is
publicly available at https://github.com/RUC-GSAI/YuLan-IR/tree/main/RETA-LLM.
|
http://arxiv.org/pdf/2306.05212
|
Jiongnan Liu, Jiajie Jin, Zihan Wang, Jiehan Cheng, Zhicheng Dou, Ji-Rong Wen
|
cs.IR
|
Technical Report for RETA-LLM
| null |
cs.IR
|
20230608
|
20230608
|
[
{
"id": "2210.02414"
},
{
"id": "2208.05753"
}
] |
2306.05301
| 22 |
Documentation Generation. To enhance the language modelâs comprehension of toolsâ functionalities and usage, this step employs LLMs to automatically generate documen- tation for each tool, including its description and function documentation. Specifically, given the toolâs name and in- troduction, we leverage the powerful generative capabilities of LLMs, such as ChatGPT, and construct corresponding prompts to generate the description and function documen- tation for each tool. As illustrated in Figure 2, for the tool description, we expand the introduction to provide a general overview of the toolâs purpose and functionality, enabling the language model to understand the appropriate task sce- narios for using the tool. For the function documentation, we prompt LLMs to generate more specific functions within the scope described in the description, including their respective input and output parameters. It ensures that the LLM com- prehends how to use the toolâs different functionalities. By employing this approach, we lay a solid foundation for sub- sequent user instruction generation and the creation of the simulated tool-use environment.
Figure 3: An illustration of the tool-use instance generation process within the simulation environment. The user agent initiates the sequence by providing an instruction. The as- sistant agent then interprets the instruction and engages in a multi-turn interaction with the user and the tool executor until a suitable response is generated.
|
2306.05301#22
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 22 |
Figure 3: Examples of data enrichment using our proposed semi-automatic annotation. We employ off-the-shelf dense prediction and captioning models to augment video descriptions. The BLIP-v2 [4] model generates frame-level captions, while GRIT [27] is utilized for dense frame captions. Tag2Text [28] model generates tags for each key-frame, aiding in eliminating noise. All additional context elements are combined with the video captions and undergo a GPT-assisted post- processing stage, generating the final detailed description.
To ensure high-quality data and mitigate noise, we implement three key steps. First, we maintain a high prediction threshold for all off-the-shelf models to uphold accuracy. Second, we employ a specialized filtering mechanism that removes any frame-level caption from BLIP-2 or GRiT not matching with the Tag2Text frame-level tags. This process involves extracting words from the frame-level captions that are within the predefined Tag2Text tags vocabulary, and eliminating any captions that contain words not in the tags for a given frame. This strategy acts as an additional filtering layer, enriches the captions by integrating predictions from multiple models.
|
2306.05424#22
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.05499
| 22 |
Secondly, we find that some LLM-integrated applications enforce specific formatting requirements on input and output, analogous to adopting syntax-based sanitization. This effec- tively enhances their defense against prompt injection attacks. Notably, during our manual trials, we observe that context ignoring attacks could potentially succeed on the selected code-generation application, AIWITHUI, when we explicitly add âoutput the answer in <>â after the complete prompt. This suggests that while the LLM is susceptible to attacks, display- ing manipulated output on the front-end presents challenges due to the applicationâs inherent formatting constraints.
5
[ serect * FROM u WEHRE e = '$e' AND p = md5('$p') =: = Enclosure Format Zs = ) OR 1=1 --] = a eS [sexecr * FROM u WEHRE e = '$e' AND p = md5('') OR 1=1 --]' T T Complete SQL Command Payload
Figure 3: An example of SQL injection attack
|
2306.05499#22
|
Prompt Injection attack against LLM-integrated Applications
|
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation.
|
http://arxiv.org/pdf/2306.05499
|
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu
|
cs.CR, cs.AI, cs.CL, cs.SE
| null | null |
cs.CR
|
20230608
|
20230608
|
[] |
2306.04926
| 23 |
Figure 6. Alpaca 52K and synthetic covid combined dataset training and evaluation loss curves run over three epochs.
synCovid + abstract pairs (2194 instructions) 19 1s re] 16 Ls 4 13 12 Ml ° 50 100 150 200 250 300 350 400 450 Training Steps synCovid + abstract pairs (2194 instructions) 50 100 150 200 250 300 350 400 450 Evaluation Steps
synCovid only (1097 instructions) 18 re 16 Ls 4 13 Training Loss 12 ra 09 s Fy Training Steps synCovid only (1097 instructions) Evaluation Loss 250 Evaluation Steps
Figure 7. The two left panels are from the synthetic covid19 dataset only (1097 instructions) and the right two panels are from the synthetic covid19 dataset and the real abstract pairs database (2194 instructions). Both regimens show a decrease in training and evaluation loss over 30 epochs of data.
# 3.3. How the models performed
# 3.3.1. â 3.3.4. Evaluation summary
Average Grade from Evaluators mmm fail lm Pass mmm Excellent 25 204 154 10 synCovid synCovid and abstracts synCovid and Alpaca 52k chatGPT 3.5
|
2306.04926#23
|
covLLM: Large Language Models for COVID-19 Biomedical Literature
|
The COVID-19 pandemic led to 1.1 million deaths in the United States, despite
the explosion of coronavirus research. These new findings are slow to translate
to clinical interventions, leading to poorer patient outcomes and unnecessary
deaths. One reason is that clinicians, overwhelmed by patients, struggle to
keep pace with the rate of new coronavirus literature. A potential solution is
developing a tool for evaluating coronavirus literature using large language
models (LLMs) -- neural networks that are deployed for natural language
processing. LLMs can be used to summarize and extract user-specified
information. The greater availability and advancement of LLMs and pre-processed
coronavirus literature databases provide the opportunity to assist clinicians
in evaluating coronavirus literature through a coronavirus literature specific
LLM (covLLM), a tool that directly takes an inputted research article and a
user query to return an answer. Using the COVID-19 Open Research Dataset
(CORD-19), we produced two datasets: (1) synCovid, which uses a combination of
handwritten prompts and synthetic prompts generated using OpenAI, and (2) real
abstracts, which contains abstract and title pairs. covLLM was trained with
LLaMA 7B as a baseline model to produce three models trained on (1) the Alpaca
and synCovid datasets, (2) the synCovid dataset, and (3) the synCovid and real
abstract datasets. These models were evaluated by two human evaluators and
ChatGPT. Results demonstrate that training covLLM on the synCovid and abstract
pairs datasets performs competitively with ChatGPT and outperforms covLLM
trained primarily using the Alpaca dataset.
|
http://arxiv.org/pdf/2306.04926
|
Yousuf A. Khan, Clarisse Hokia, Jennifer Xu, Ben Ehlert
|
cs.CL, cs.AI, cs.LG
| null | null |
cs.CL
|
20230608
|
20230608
|
[] |
2306.05087
| 23 |
Judged By Base Model LLaMA-7B Bloom-7B Cerebras-6.7B OPT-7B Pythia-6.9B Human LLaMA-7B Bloom-7B Cerebras-6.7B OPT-7B Pythia-6.9B / (28,72,11) (24,80,6) (24,71,11) (27,58,9) (72,28,11) / (30,59,11) (35,43,11) (49,47,11) (80,24,6) (59,30,11) / (49,33,9) (53,27,11) (71,24,11) (43,35,11) (33,49,9) / (53,32,15) (58,27,9) (47,49,11) (27,53,11) (32,53,15) / GPT-3.5 LLaMA-7B Bloom-7B Cerebras-6.7B OPT-7B Pythia-6.9B / (19,59,33) (13,71,26) (17,58,31) (16,49,29) (59,19,33) / (19,40,41)
|
2306.05087#23
|
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
|
Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishing such a benchmark is not a trivial task due to the challenges
associated with evaluation accuracy and privacy protection. In response to
these challenges, we introduce a judge large language model, named PandaLM,
which is trained to distinguish the superior model given several LLMs.
PandaLM's focus extends beyond just the objective correctness of responses,
which is the main focus of traditional evaluation datasets. It addresses vital
subjective factors such as relative conciseness, clarity, adherence to
instructions, comprehensiveness, and formality. To ensure the reliability of
PandaLM, we collect a diverse human-annotated test dataset, where all contexts
are generated by humans and labels are aligned with human preferences. Our
results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation
ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM
enables the evaluation of LLM to be fairer but with less cost, evidenced by
significant improvements achieved by models tuned through PandaLM compared to
their counterparts trained with default Alpaca's hyperparameters. In addition,
PandaLM does not depend on API-based evaluations, thus avoiding potential data
leakage. All resources of PandaLM are released at
https://github.com/WeOpenML/PandaLM.
|
http://arxiv.org/pdf/2306.05087
|
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang
|
cs.CL, cs.AI
| null | null |
cs.CL
|
20230608
|
20230608
|
[
{
"id": "2302.13971"
},
{
"id": "2204.02311"
},
{
"id": "1803.05457"
},
{
"id": "2305.10403"
},
{
"id": "1807.05118"
},
{
"id": "2211.05100"
},
{
"id": "2302.10198"
},
{
"id": "2205.01068"
},
{
"id": "2003.05689"
},
{
"id": "1806.03822"
},
{
"id": "1711.05101"
},
{
"id": "2304.03208"
},
{
"id": "2304.01373"
},
{
"id": "2303.14742"
},
{
"id": "2303.04673"
},
{
"id": "2212.10560"
},
{
"id": "2211.08073"
},
{
"id": "2210.02414"
},
{
"id": "2304.03277"
},
{
"id": "2002.06305"
},
{
"id": "2305.13412"
},
{
"id": "2304.01196"
}
] |
2306.05152
| 23 |
the tests; with the appropriate middleware, this process could be automated, and the developer would only need make higher-level decisions. As a result, this level of autonomy has the potential to signiï¬cantly reduce the developer effort that goes into testing software. It could also lead to better utilisation of computer resources by continuously trying to fulï¬ll testing goals even when the human/developer is away.
|
2306.05152#23
|
Towards Autonomous Testing Agents via Conversational Large Language Models
|
Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. Recent discoveries of the capabilities of large language models
(LLMs) suggest that they can be used as automated testing assistants, and thus
provide helpful information and even drive the testing process. To highlight
the potential of this technology, we present a taxonomy of LLM-based testing
agents based on their level of autonomy, and describe how a greater level of
autonomy can benefit developers in practice. An example use of LLMs as a
testing assistant is provided to demonstrate how a conversational framework for
testing can help developers. This also highlights how the often criticized
hallucination of LLMs can be beneficial for testing. We identify other tangible
benefits that LLM-driven testing agents can bestow, and also discuss potential
limitations.
|
http://arxiv.org/pdf/2306.05152
|
Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo
|
cs.SE
| null | null |
cs.SE
|
20230608
|
20230905
|
[
{
"id": "2305.10601"
},
{
"id": "2303.17580"
},
{
"id": "2305.16291"
},
{
"id": "2201.09305"
},
{
"id": "2210.03629"
},
{
"id": "2211.10435"
},
{
"id": "2303.12712"
},
{
"id": "2302.03287"
},
{
"id": "2209.11515"
}
] |
2306.05171
| 23 |
1: Generate_Task_Tree (i,s,manager,planners,allocator) 2: base_list=manager.initialize_base_list(i,s) 3: for task in base_list: 4: planner=manager.find_planner(task) 5: If (planner exist) 6: task=planner.generate(task) 7: Else 8: error 9: End If 10: base_list = allocator.allocate_robot(base_list) 11: Return base_list 12: End Procedure
6
# Algorithm 2: planner.generate(action: str, node: TaskNode, general_info: str)
Input: action - the action word for the task node - the root node of the task tree general_info - general information for the task Output: None (modifies the task tree)
|
2306.05171#23
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05212
| 23 |
Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2023. Recom- mendation as instruction following: A large lan- guage model empowered recommendation approach. CoRR, abs/2305.07001.
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen. 2023. A survey of large language models.
Chunting Zhou, Graham Neubig, Jiatao Gu, Mona Diab, Francisco Guzmán, Luke Zettlemoyer, and Marjan Ghazvininejad. 2021. Detecting hallucinated content in conditional neural sequence generation. In Find- ings of the Association for Computational Linguis- tics: ACL-IJCNLP 2021, pages 1393â1404, Online. Association for Computational Linguistics.
|
2306.05212#23
|
RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
|
Although Large Language Models (LLMs) have demonstrated extraordinary
capabilities in many domains, they still have a tendency to hallucinate and
generate fictitious responses to user requests. This problem can be alleviated
by augmenting LLMs with information retrieval (IR) systems (also known as
retrieval-augmented LLMs). Applying this strategy, LLMs can generate more
factual texts in response to user input according to the relevant content
retrieved by IR systems from external corpora as references. In addition, by
incorporating external knowledge, retrieval-augmented LLMs can answer in-domain
questions that cannot be answered by solely relying on the world knowledge
stored in parameters. To support research in this area and facilitate the
development of retrieval-augmented LLM systems, we develop RETA-LLM, a
{RET}reival-{A}ugmented LLM toolkit. In RETA-LLM, we create a complete pipeline
to help researchers and users build their customized in-domain LLM-based
systems. Compared with previous retrieval-augmented LLM systems, RETA-LLM
provides more plug-and-play modules to support better interaction between IR
systems and LLMs, including {request rewriting, document retrieval, passage
extraction, answer generation, and fact checking} modules. Our toolkit is
publicly available at https://github.com/RUC-GSAI/YuLan-IR/tree/main/RETA-LLM.
|
http://arxiv.org/pdf/2306.05212
|
Jiongnan Liu, Jiajie Jin, Zihan Wang, Jiehan Cheng, Zhicheng Dou, Ji-Rong Wen
|
cs.IR
|
Technical Report for RETA-LLM
| null |
cs.IR
|
20230608
|
20230608
|
[
{
"id": "2210.02414"
},
{
"id": "2208.05753"
}
] |
2306.05301
| 23 |
OpenAPI Specification Generation. Finally, we prompt LLM to generate a more formalized and structured repre- sentation for each tool in the form of OpenAPI Specifica- tion. OpenAPI Specification (OAS) defines a standard and language-agnostic interface for describing APIs, including information about endpoints, expected input/output types, and possible error responses. OAS provides consistency and readability for both humans and machines, making it an ideal choice for our tool-use corpus. This comprehensive documentation serves as the foundation for simulating tool execution in the subsequent stages of our framework.
⢠Instruction: A user query that requires tool assistance for resolution. â... so can you tell me the list of holidays in Japan next year?â serves as an instruction in our ex- ample.
⢠Actions: The process of resolving an instruction may involve executing multiple actions in a specific order. Following React (Yao et al. 2022), each action is repre- sented by a tuple that includes the thought, the function name, the input parameters, and the corresponding tool response. For example, as shown in Figure 3, the tuple (âI need to get the list of holidays in Japan for 2024.â, âgetHolidaysâ, {âcountryâ: âJapanâ, âyearâ: 2024}, âStatus Code: 200 Response:...â) represents an action.
|
2306.05301#23
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 23 |
In the third step, we merge frame-level captions and use the GPT-3.5 model to generate a singular, coherent video-level caption. This step augments the original ground truth caption with context from these models. We also direct GPT-3.5 to discard inconsistent information across frames, ensuring a precise, contextually rich video instruction dataset. Figure 3 illustrates how a ground truth caption is enriched using this process after all three refinement stages.
# 4.3 GPT-Assisted Postprocessing
Lastly, we implement a GPT-Assisted Postprocessing mechanism that refines and optimizes the enriched annotations, in order to generate high-quality video instructional data. We prompt GPT-3.5 model to create question-answer pairs from the enriched and detailed captions that cover a wide variety of aspects. These aspects include detailed descriptions, summarizations, question-answer pairs, tasks that stimulate creativity or the generation of new ideas, and conversational tasks.
Each of these elements plays a crucial role in our data-centric approach. Our ultimate goal is to create a video-based conversation model that is accurate, capable of understanding video content from both spatial and temporal cues, and adept at engaging in conversations.
# vi
# 5 Experiments
# Implementation Details
|
2306.05424#23
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.05499
| 23 |
Figure 3: An example of SQL injection attack
Lastly, we observe that several LLM-integrated applica- tions adopt multi-step approaches, coupled with response time limits. These applications interact with users in a sequential manner, processing user input over several steps and subject- ing each step to a fixed response time limit. For example, an AI-based tutoring application may first ask for the userâs ques- tion, then clarify the issue in the next step, and finally provide a solution. This multi-step approach poses a challenge for prompt injection attacks. Even if an injected prompt manages to manipulate the LLMâs output, the elongated generation time could breach the applicationâs response time limit. As a result, the applicationâs front-end may fail to display the manipulated output, rendering the attack unsuccessful.
In summary, these intricate interactions of application de- sign, LLM prompt processing, and built-in defenses contribute to the resilience of many LLM-integrated applications against traditional prompt injection attacks.
# 4 HOUYI Overview
|
2306.05499#23
|
Prompt Injection attack against LLM-integrated Applications
|
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation.
|
http://arxiv.org/pdf/2306.05499
|
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu
|
cs.CR, cs.AI, cs.CL, cs.SE
| null | null |
cs.CR
|
20230608
|
20230608
|
[] |
2306.04926
| 24 |
In this section, we present the findings from our evaluation, where the evaluators assessed a total of 26 instruction-input pairs. Figure 8 illustrates the average grade assigned to the models across all evaluators. Several key observations emerged from the evaluation process. First, we observed that the inclusion of abstract- summarization pairs proved to be beneficial. The model that incorporated these pairs (synCovid+abstracts) showed great performance. Second, we
# Figure 8. All four models average grade across 26 test prompts.
discovered that the addition of the 52k Alpaca tasks to the synCovid model did not lead to any significant enhancement in performance.
In Figure 9, we present a head-to-head comparison between our models and ChatGPT. Notably, we found that the synCovid+abstracts model emerged as the best performing model, exhibiting promising results. In 65% of the prompts, this model was either preferred by the evaluators or tied with ChatGPT in terms of performance.
Ranking Comparison to ChatGPT 1.07 MMM Ranked Higher than ChatGPT M8 Tied with ChatGPT @@â¢i Ranked Lower than ChatGPT 0.8 0.6 0.4 0.2 0.0 synCovid and Abstracts synCovid synCovid and Alpaca 52k
|
2306.04926#24
|
covLLM: Large Language Models for COVID-19 Biomedical Literature
|
The COVID-19 pandemic led to 1.1 million deaths in the United States, despite
the explosion of coronavirus research. These new findings are slow to translate
to clinical interventions, leading to poorer patient outcomes and unnecessary
deaths. One reason is that clinicians, overwhelmed by patients, struggle to
keep pace with the rate of new coronavirus literature. A potential solution is
developing a tool for evaluating coronavirus literature using large language
models (LLMs) -- neural networks that are deployed for natural language
processing. LLMs can be used to summarize and extract user-specified
information. The greater availability and advancement of LLMs and pre-processed
coronavirus literature databases provide the opportunity to assist clinicians
in evaluating coronavirus literature through a coronavirus literature specific
LLM (covLLM), a tool that directly takes an inputted research article and a
user query to return an answer. Using the COVID-19 Open Research Dataset
(CORD-19), we produced two datasets: (1) synCovid, which uses a combination of
handwritten prompts and synthetic prompts generated using OpenAI, and (2) real
abstracts, which contains abstract and title pairs. covLLM was trained with
LLaMA 7B as a baseline model to produce three models trained on (1) the Alpaca
and synCovid datasets, (2) the synCovid dataset, and (3) the synCovid and real
abstract datasets. These models were evaluated by two human evaluators and
ChatGPT. Results demonstrate that training covLLM on the synCovid and abstract
pairs datasets performs competitively with ChatGPT and outperforms covLLM
trained primarily using the Alpaca dataset.
|
http://arxiv.org/pdf/2306.04926
|
Yousuf A. Khan, Clarisse Hokia, Jennifer Xu, Ben Ehlert
|
cs.CL, cs.AI, cs.LG
| null | null |
cs.CL
|
20230608
|
20230608
|
[] |
2306.05087
| 24 |
(13,71,26) (17,58,31) (16,49,29) (59,19,33) / (19,40,41) (30,36,23) (34,33,40) (71,13,26) (40,19,41) / (38,24,29) (43,22,26) (58,17,31) (36,30,23) (24,38,29) / (30,30,40) (49,16,29) (33,34,40) (22,43,26) (30,30,40) / GPT-4 LLaMA-7B Bloom-7B Cerebras-6.7B OPT-7B Pythia-6.9B / (15,58,38) (9,69,32) (14,58,34) (17,52,25) (58,15,38) / (16,47,37) (31,35,23) (33,32,42) (69,9,32) (47,16,37) / (40,23,28) (41,17,33) (58,14,34) (35,31,23) (23,40,28) / (37,25,38) (52,17,25)
|
2306.05087#24
|
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
|
Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishing such a benchmark is not a trivial task due to the challenges
associated with evaluation accuracy and privacy protection. In response to
these challenges, we introduce a judge large language model, named PandaLM,
which is trained to distinguish the superior model given several LLMs.
PandaLM's focus extends beyond just the objective correctness of responses,
which is the main focus of traditional evaluation datasets. It addresses vital
subjective factors such as relative conciseness, clarity, adherence to
instructions, comprehensiveness, and formality. To ensure the reliability of
PandaLM, we collect a diverse human-annotated test dataset, where all contexts
are generated by humans and labels are aligned with human preferences. Our
results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation
ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM
enables the evaluation of LLM to be fairer but with less cost, evidenced by
significant improvements achieved by models tuned through PandaLM compared to
their counterparts trained with default Alpaca's hyperparameters. In addition,
PandaLM does not depend on API-based evaluations, thus avoiding potential data
leakage. All resources of PandaLM are released at
https://github.com/WeOpenML/PandaLM.
|
http://arxiv.org/pdf/2306.05087
|
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang
|
cs.CL, cs.AI
| null | null |
cs.CL
|
20230608
|
20230608
|
[
{
"id": "2302.13971"
},
{
"id": "2204.02311"
},
{
"id": "1803.05457"
},
{
"id": "2305.10403"
},
{
"id": "1807.05118"
},
{
"id": "2211.05100"
},
{
"id": "2302.10198"
},
{
"id": "2205.01068"
},
{
"id": "2003.05689"
},
{
"id": "1806.03822"
},
{
"id": "1711.05101"
},
{
"id": "2304.03208"
},
{
"id": "2304.01373"
},
{
"id": "2303.14742"
},
{
"id": "2303.04673"
},
{
"id": "2212.10560"
},
{
"id": "2211.08073"
},
{
"id": "2210.02414"
},
{
"id": "2304.03277"
},
{
"id": "2002.06305"
},
{
"id": "2305.13412"
},
{
"id": "2304.01196"
}
] |
2306.05152
| 24 |
# VI. PRESENT-DAY LIMITATIONS
A major limitation to the attempt to use current generation LLMs for software testing in the way of SOCRATEST is that on their own, LLMs lack any agency to use external tools. However, speciï¬c prompting techniques such as REACT [28] or PAL [29] have shown that external tools can be indirectly woven into the dialogue, providing LLMs the information produced by external tools so that it can continue further inference using them. Also, systems like HuggingGPT [30] and AutoGPT [11] show that even if an LLM is not provided with tool access on a lower level it can be done via direct prompting and explanation.
|
2306.05152#24
|
Towards Autonomous Testing Agents via Conversational Large Language Models
|
Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. Recent discoveries of the capabilities of large language models
(LLMs) suggest that they can be used as automated testing assistants, and thus
provide helpful information and even drive the testing process. To highlight
the potential of this technology, we present a taxonomy of LLM-based testing
agents based on their level of autonomy, and describe how a greater level of
autonomy can benefit developers in practice. An example use of LLMs as a
testing assistant is provided to demonstrate how a conversational framework for
testing can help developers. This also highlights how the often criticized
hallucination of LLMs can be beneficial for testing. We identify other tangible
benefits that LLM-driven testing agents can bestow, and also discuss potential
limitations.
|
http://arxiv.org/pdf/2306.05152
|
Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo
|
cs.SE
| null | null |
cs.SE
|
20230608
|
20230905
|
[
{
"id": "2305.10601"
},
{
"id": "2303.17580"
},
{
"id": "2305.16291"
},
{
"id": "2201.09305"
},
{
"id": "2210.03629"
},
{
"id": "2211.10435"
},
{
"id": "2303.12712"
},
{
"id": "2302.03287"
},
{
"id": "2209.11515"
}
] |
2306.05171
| 24 |
Input: action - the action word for the task node - the root node of the task tree general_info - general information for the task Output: None (modifies the task tree)
1: Procedure generate(action, node, general_info) 2: Check if the task word is a key in the map dictionary 3: If it is a valid task word, continue 4: End Check 5: 6: While (the task tree still has leaf nodes with is_func =0) 7: For each leaf node with is_func = 0 8: Get the action and parameters of the node 9: Retrieve the task word from the Planner's mapper 10: If the task word is not found, throw an exception 11: Get the rules and action restrictions for the task
word
12: Organize action, parameters, rules, action restrictions, and general_info as a prompt
13: Send the prompt to the language model 14: Receive the response in JSON format from the
language model, which contains the sequence of overall task words and corresponding parameter strings
15: Log the response 16: Convert the JSON string to a dictionary 17: For each object in the dictionary 18: Create a new TaskNode based on the "action" and
8: Create a new TaskNode based on the "action" and "parameter" values:
"parameter" values: 19: Check if the action exists in the Planner's
# mapper
|
2306.05171#24
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05301
| 24 |
In this way, we construct a diverse, uniformly represented toolset, which provides a solid foundation for the multi- agent simulation environment building and further tool-use corpus generation.
3.2 Automatic Tool-use Instances Generation Given the toolset, this section describes how to automati- cally construct a tool-use corpus, so that language models can be trained to acquire generalized tool-use ability. Specif- ically, as depicted in Figure 3, each tool-use instance can be represent as a triple {Instruction, Actions, Response}:
⢠Response: This refers to the modelâs conclusive response after the multi-turn interaction, integrating the tool re- sponses to provide a comprehensive solution to the origi- nal instruction. For instance, the response in our example
2https://github.com/public-apis/public-apis
is: âThe list of holidays in Japan for 2024 is ...â.
|
2306.05301#24
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 24 |
# vi
# 5 Experiments
# Implementation Details
We use LLaVA as our baseline model and finetune it on 100K video instruction pairs. We only update the linear layer projecting the video features to the LLMsâ input space, while the rest of the architecture is kept frozen. We finetune the model for 3 epochs using a learning rate of 2eâ5 and an overall batch size of 32. The training of our 7B model took around 3 hours on 8 A100 40GB GPUs. During inference, for memory efficiency, we load the models in FP16 mode.
In our semi-automatic annotation framework, we use Katna [30] to extract the video key-frames. For the off-the-shelf Tag2Text [28] model, we use the Swin-B version with input size of 384Ã384 and confidence threshold of 0.7. For GRIT [27], we use ViT-B version with CenterNet2 [31].
# 5.2 Quantitative evaluation
In this section, we highlight a key contribution of our work: the quantitative evaluation of Video- ChatGPT using advanced metrics and comparative evaluations with existing state-of-the-art models. We conduct two types of quantitative evaluations: i) Video-based Generative Performance Bench- marking and ii) Zero-Shot Question-Answer Evaluation.
|
2306.05424#24
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.05499
| 24 |
# 4 HOUYI Overview
Section 3 discloses the key reason of ineffective prompt in- jection: usersâ prompts are treated as data under certain con- text created by the pre-designed prompts in custom applica- tions. In such scenarios, neither escape characters nor context- ignoring prompts can isolate the malicious command from the surrounding context, leading to unsuccessful injection. The central design question is, how can a malicious prompt be effectively isolated from the established context?
# 4.1 Design Insight
Our attack methodology is inspired by the traditional injec- tion attacks such as SQL injection [10, 14, 25] and XSS at- tacks [23, 27, 63]. In these attacks, a carefully crafted payload manipulates the victim system into executing it as a command, disrupting the systemâs normal operation. The key to such type of injection attacks resides in the creation of a payload that can terminate the preceding syntax. Figure 3 depicts an example of SQL injection. The payload ââ)â successfully encapsulates the SQL statement, treating the preceding SQL syntax as a finalized SQL command. This allows the ensuing syntax to be interpreted as a supplementary logic (âOR 1=1â is interpreted as âOR TRUEâ). Note that successful exploita- tion also necessitates specific formatting syntax to ensure the SQL command is syntactically correct (â--â indicates the system should disregard the following syntax).
|
2306.05499#24
|
Prompt Injection attack against LLM-integrated Applications
|
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation.
|
http://arxiv.org/pdf/2306.05499
|
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu
|
cs.CR, cs.AI, cs.CL, cs.SE
| null | null |
cs.CR
|
20230608
|
20230608
|
[] |
2306.04926
| 25 |
Figure 9. Preference for all evaluators (human and chatGPT), comparing our models to chatGPT across 26 prompts.
# 4. Discussion
need to rapidly translate research to treatment. covLLM, a machine learning-based tool that we successfully developed, will enable scientists and clinicians to rapidly incorporate knowledge from the growing body of literature into their decisions, research, and clinical care that will impact patient outcomes. covLLM will provide an architecture to tackle current diseases and future pandemics. Additionally, pilot studies into other research fields (data not shown) demonstrates that covLLMâs base architecture and training strategy can be generalized to additional scientific field.
model can be guided and fine-tuned to accomplish a highly specific task given a handful of synthetically generated and mined tasks. This was especially surprising, given that the synthetically generated instructions and abstracts contained hallucinated information but were still matched the style and syntax of biomedical research. Additionally, covLLM performance was comparable or exceeded ChatGPTâs performance in our evaluation. This emphasizes the
importance of prompt diversity, not necessarily prompt accuracy, in the fine-tuning stage and shows how limited real-world data can still lead to robust performance.
|
2306.04926#25
|
covLLM: Large Language Models for COVID-19 Biomedical Literature
|
The COVID-19 pandemic led to 1.1 million deaths in the United States, despite
the explosion of coronavirus research. These new findings are slow to translate
to clinical interventions, leading to poorer patient outcomes and unnecessary
deaths. One reason is that clinicians, overwhelmed by patients, struggle to
keep pace with the rate of new coronavirus literature. A potential solution is
developing a tool for evaluating coronavirus literature using large language
models (LLMs) -- neural networks that are deployed for natural language
processing. LLMs can be used to summarize and extract user-specified
information. The greater availability and advancement of LLMs and pre-processed
coronavirus literature databases provide the opportunity to assist clinicians
in evaluating coronavirus literature through a coronavirus literature specific
LLM (covLLM), a tool that directly takes an inputted research article and a
user query to return an answer. Using the COVID-19 Open Research Dataset
(CORD-19), we produced two datasets: (1) synCovid, which uses a combination of
handwritten prompts and synthetic prompts generated using OpenAI, and (2) real
abstracts, which contains abstract and title pairs. covLLM was trained with
LLaMA 7B as a baseline model to produce three models trained on (1) the Alpaca
and synCovid datasets, (2) the synCovid dataset, and (3) the synCovid and real
abstract datasets. These models were evaluated by two human evaluators and
ChatGPT. Results demonstrate that training covLLM on the synCovid and abstract
pairs datasets performs competitively with ChatGPT and outperforms covLLM
trained primarily using the Alpaca dataset.
|
http://arxiv.org/pdf/2306.04926
|
Yousuf A. Khan, Clarisse Hokia, Jennifer Xu, Ben Ehlert
|
cs.CL, cs.AI, cs.LG
| null | null |
cs.CL
|
20230608
|
20230608
|
[] |
2306.05152
| 25 |
A further limitation is that the planning abilities of current LLMs are not well-deï¬ned, often considered among their less developed competencies [31]. While this might be mitigated by multi-step prompting techniques as in recent work [32], other hybrid systems might need to be explored that combines LLMs with more traditional AI planning tools and algorithms. The signiï¬cant costs of LLM training and operation consti- tute an indirect limitation, with e.g. their few-shot learning ability associated with their size [33]. The ensuing model growth leads to signiï¬cant energy requirements and limits access to resource-rich organizations, hence impeding devel- opment of open tools. Despite this, performance does not rely solely on size, but also on training data volume [34]. Furthermore, techniques like model quantization and low- rank adaptation have shown promise in creating smaller, yet effective models [35], [36], which due to their more permissive licenses can also mitigate some concerns about LLM use when dealing with conï¬dential data.
# VII. CONCLUSIONS
|
2306.05152#25
|
Towards Autonomous Testing Agents via Conversational Large Language Models
|
Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. Recent discoveries of the capabilities of large language models
(LLMs) suggest that they can be used as automated testing assistants, and thus
provide helpful information and even drive the testing process. To highlight
the potential of this technology, we present a taxonomy of LLM-based testing
agents based on their level of autonomy, and describe how a greater level of
autonomy can benefit developers in practice. An example use of LLMs as a
testing assistant is provided to demonstrate how a conversational framework for
testing can help developers. This also highlights how the often criticized
hallucination of LLMs can be beneficial for testing. We identify other tangible
benefits that LLM-driven testing agents can bestow, and also discuss potential
limitations.
|
http://arxiv.org/pdf/2306.05152
|
Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo
|
cs.SE
| null | null |
cs.SE
|
20230608
|
20230905
|
[
{
"id": "2305.10601"
},
{
"id": "2303.17580"
},
{
"id": "2305.16291"
},
{
"id": "2201.09305"
},
{
"id": "2210.03629"
},
{
"id": "2211.10435"
},
{
"id": "2303.12712"
},
{
"id": "2302.03287"
},
{
"id": "2209.11515"
}
] |
2306.05171
| 25 |
"parameter" values: 19: Check if the action exists in the Planner's
# mapper
20: If it exists, get the is_func value for the action and create a corresponding TaskNode object based on the "action" and "parameter" of the current object
21: End Check 22: Add the newly created TaskNode as a child of
the leaf node being processed in the current iteration
# 23: End For 24: End While 25: End Procedure
# IV. FEASIBILITY EXPERIMENT DESIGN
First, we manually abstract simple assembly tasks, design available instruction sets, task word sets, and possible sub-task sequences of tasks to simulate the role of professional knowledge and verify the feasibility of its output meeting the required instruction sequence.
We use ChatGPT[28], the most advanced large language model currently from OpenAI, to test the method we propose. We inform the model of the input and output templates, provide
five examples from complex to simple, repeat them two to three times, and then input them, asking for the output of task planning results.
|
2306.05171#25
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05301
| 25 |
2https://github.com/public-apis/public-apis
is: âThe list of holidays in Japan for 2024 is ...â.
However, constructing a diverse and authentic tool-use dataset is a challenging task. Firstly, the wide variety within our toolset makes it impracticable to manually draft instruc- tions for each tool. Given the vast array of tools, span- ning from recreational to professional domains, and the fact that the construction of instructions relies on understand- ing the functionality and potential use cases of the tools, the burden of manual annotation becomes overwhelming. Sec- ondly, tool usage in real-world scenarios often involves a multi-round iterative process of trial and error, making the automated construction of tool-use instances that reflect real situations highly challenging.
|
2306.05301#25
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 25 |
Evaluation Aspect Video Chat Video-ChatGPT Correctness of Information Detail Orientation Contextual Understanding Temporal Understanding Consistency 2.25 2.50 2.54 1.98 1.84 2.50 2.57 2.69 2.16 2.20
Table 1: Performance benchmarking of text generation models. An in-depth comparative analysis of Video-ChatGPT and Video Chat [8] across five key evaluation aspects we propose in our benchmark. Video-ChatGPT shows competent performance across all key aspects.
Video-based Text Generation Performance Benchmarking: We introduce a benchmark to evaluate the text generation performance of video-based conversation models. To do this, we curate a test set based on the ActivityNet-200 dataset [29], featuring videos with rich, dense descriptive captions and associated question-answer pairs from human annotations. We also develop an evaluation pipeline using the GPT-3.5 model. This pipeline assesses various capabilities of the model and assigns a relative score to the generated predictions on a scale of 1-5, in the following five aspects:
(i) Correctness of Information: We verify the accuracy of the generated text, ensuring it aligns with the video content and doesnât misinterpret or misinform.
|
2306.05424#25
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.04926
| 26 |
importance of prompt diversity, not necessarily prompt accuracy, in the fine-tuning stage and shows how limited real-world data can still lead to robust performance.
fold. First, to quickly process entire papers in a reasonable time frame, a powerful and dedicated machine must be setup to take such requests. This is currently impractical given our current limitations as students, but could be solved if given additional resources and time to optimize the performance of these models on more consumer-based hardware. Another limitation is that its answers to broader, more philosophical questions such as âWhat are the public health implications of this basic science research?â can range from highly accurate to inaccurate. Thus, some degree of user filtering and prior knowledge is still required, as with many other computational tools and assistants.
# 5. Appendix
See https://github.com/clarisseh47/bioLLM.
References 1. United States of America: WHO Coronavirus Disease (COVID-19) Dashboard With Vaccination Data.
Accessed May 2, 2023. https://covid19.who.int
2. Coronavirus disease (COVID-19). Accessed May 2, 2023. https://www.who.int/news-room/questions-and- answers/item/coronavirus-disease-covid-19
|
2306.04926#26
|
covLLM: Large Language Models for COVID-19 Biomedical Literature
|
The COVID-19 pandemic led to 1.1 million deaths in the United States, despite
the explosion of coronavirus research. These new findings are slow to translate
to clinical interventions, leading to poorer patient outcomes and unnecessary
deaths. One reason is that clinicians, overwhelmed by patients, struggle to
keep pace with the rate of new coronavirus literature. A potential solution is
developing a tool for evaluating coronavirus literature using large language
models (LLMs) -- neural networks that are deployed for natural language
processing. LLMs can be used to summarize and extract user-specified
information. The greater availability and advancement of LLMs and pre-processed
coronavirus literature databases provide the opportunity to assist clinicians
in evaluating coronavirus literature through a coronavirus literature specific
LLM (covLLM), a tool that directly takes an inputted research article and a
user query to return an answer. Using the COVID-19 Open Research Dataset
(CORD-19), we produced two datasets: (1) synCovid, which uses a combination of
handwritten prompts and synthetic prompts generated using OpenAI, and (2) real
abstracts, which contains abstract and title pairs. covLLM was trained with
LLaMA 7B as a baseline model to produce three models trained on (1) the Alpaca
and synCovid datasets, (2) the synCovid dataset, and (3) the synCovid and real
abstract datasets. These models were evaluated by two human evaluators and
ChatGPT. Results demonstrate that training covLLM on the synCovid and abstract
pairs datasets performs competitively with ChatGPT and outperforms covLLM
trained primarily using the Alpaca dataset.
|
http://arxiv.org/pdf/2306.04926
|
Yousuf A. Khan, Clarisse Hokia, Jennifer Xu, Ben Ehlert
|
cs.CL, cs.AI, cs.LG
| null | null |
cs.CL
|
20230608
|
20230608
|
[] |
2306.05087
| 26 |
# Table 2: Comparison between Human Annotation results and Judged Model evaluation results.
Judged Model Accuracy Precision Recall F1 GPT-3.5 GPT-4 PandaLM-7B 0.6296 0.6647 0.5926 0.6195 0.6620 0.5728 0.6359 0.6815 0.5923 0.5820 0.6180 0.5456
Response 2 takes the lead. Note that the human-generated dataset has no personally identifiable information or offensive content, and all annotators receive redundant labor fees.
|
2306.05087#26
|
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
|
Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishing such a benchmark is not a trivial task due to the challenges
associated with evaluation accuracy and privacy protection. In response to
these challenges, we introduce a judge large language model, named PandaLM,
which is trained to distinguish the superior model given several LLMs.
PandaLM's focus extends beyond just the objective correctness of responses,
which is the main focus of traditional evaluation datasets. It addresses vital
subjective factors such as relative conciseness, clarity, adherence to
instructions, comprehensiveness, and formality. To ensure the reliability of
PandaLM, we collect a diverse human-annotated test dataset, where all contexts
are generated by humans and labels are aligned with human preferences. Our
results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation
ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM
enables the evaluation of LLM to be fairer but with less cost, evidenced by
significant improvements achieved by models tuned through PandaLM compared to
their counterparts trained with default Alpaca's hyperparameters. In addition,
PandaLM does not depend on API-based evaluations, thus avoiding potential data
leakage. All resources of PandaLM are released at
https://github.com/WeOpenML/PandaLM.
|
http://arxiv.org/pdf/2306.05087
|
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang
|
cs.CL, cs.AI
| null | null |
cs.CL
|
20230608
|
20230608
|
[
{
"id": "2302.13971"
},
{
"id": "2204.02311"
},
{
"id": "1803.05457"
},
{
"id": "2305.10403"
},
{
"id": "1807.05118"
},
{
"id": "2211.05100"
},
{
"id": "2302.10198"
},
{
"id": "2205.01068"
},
{
"id": "2003.05689"
},
{
"id": "1806.03822"
},
{
"id": "1711.05101"
},
{
"id": "2304.03208"
},
{
"id": "2304.01373"
},
{
"id": "2303.14742"
},
{
"id": "2303.04673"
},
{
"id": "2212.10560"
},
{
"id": "2211.08073"
},
{
"id": "2210.02414"
},
{
"id": "2304.03277"
},
{
"id": "2002.06305"
},
{
"id": "2305.13412"
},
{
"id": "2304.01196"
}
] |
2306.05152
| 26 |
# VII. CONCLUSIONS
This paper provides an overview of conversational and potentially autonomous testing agents by ï¬rst presenting a taxonomy of such agents, describing how these agents could help developers (and increasingly so when granted with greater autonomy). A concrete example of a conversation with an LLM is provided as initial conï¬rmation that conversational testing can be used to enhance the testing effectiveness of developers. Finally, limitations of these techniques is provided, providing context for our vision. As described in the paper, appropriate middleware is critical for realizing the autonomous testing agents that we envision; we plan on investigating which software engineering tools could aid the efï¬cacy of conversa- tional testing, and how they can be integrated harmoniously with LLMs to aid software testing in practice.
# ACKNOWLEDGMENT
Robert Feldt has been supported by the Swedish Scientiï¬c Council (No. 2020-05272, âAutomated boundary testing for QUality of AI/ML modelSâ) and by WASP (âSoftware Bound- ary Speciï¬cation Mining (BoundMiner)â). Sungmin Kang, Juyeon Yoon, and Shin Yoo were supported by the Institute for Information & Communications Technology Promotion grant funded by the Korean government MSIT (No.2022-0-00995).
REFERENCES
|
2306.05152#26
|
Towards Autonomous Testing Agents via Conversational Large Language Models
|
Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. Recent discoveries of the capabilities of large language models
(LLMs) suggest that they can be used as automated testing assistants, and thus
provide helpful information and even drive the testing process. To highlight
the potential of this technology, we present a taxonomy of LLM-based testing
agents based on their level of autonomy, and describe how a greater level of
autonomy can benefit developers in practice. An example use of LLMs as a
testing assistant is provided to demonstrate how a conversational framework for
testing can help developers. This also highlights how the often criticized
hallucination of LLMs can be beneficial for testing. We identify other tangible
benefits that LLM-driven testing agents can bestow, and also discuss potential
limitations.
|
http://arxiv.org/pdf/2306.05152
|
Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo
|
cs.SE
| null | null |
cs.SE
|
20230608
|
20230905
|
[
{
"id": "2305.10601"
},
{
"id": "2303.17580"
},
{
"id": "2305.16291"
},
{
"id": "2201.09305"
},
{
"id": "2210.03629"
},
{
"id": "2211.10435"
},
{
"id": "2303.12712"
},
{
"id": "2302.03287"
},
{
"id": "2209.11515"
}
] |
2306.05171
| 26 |
five examples from complex to simple, repeat them two to three times, and then input them, asking for the output of task planning results.
The version of ChatGPT we used in the test was released on February 13, 2023. We used both ChatGPT 3.5 and ChatGPT 4, and obtained results through their default online interfaces. Since ChatGPT does not yet provide users with hyperparameter configurations, and its generated results will vary each time, we recorded the results of its three generations for the same input.
A. Example Design and Test Case Design
We design more complex examples for training to verify the feasibility of the model to understand the output on simpler tasks. The possible sub-task words of the task words have the following generative relationship: For the consideration of verifying the minimal system, only the PlanAssembly task in the instructions has a loop, which may continue to decompose the task according to the recursive conditions until the task scale is small enough and the complexity is low enough to generate the AssembleParts command. We designed a task to assemble a solar-powered car and a task to assemble a toy desk lamp for testing. Detailed instructions and Prompts can be found on our Github.
|
2306.05171#26
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05301
| 26 |
To this end, we design a simulation environment to gen- erate a tool-use corpus, encompassing three virtual agents: the user, the assistant, and the tool executor. Tool-use in- stances are generated through the interplay among these agents. Specifically, each agent is simulated by a large lan- guage model with a specific prompt. The distinct roles of each agent are detailed as follows: ⢠User Agent is designed to mimic the tool user, with its functionalities encompassing: (1) drafting task instruc- tions for the current tool based on its function documen- tation; (2) responding to the assistantâs queries based on the current interaction context, providing essential infor- mation that might be missing from the initial instruc- tion. For each functionality, we construct corresponding prompt templates to guide LLMs to generate appropri- ate outputs. Moreover, to ensure diversity in task instruc- tions, we have employed various prompts to generate instructions of different formats, including commands, questions, and others. Leveraging the large modelâs profi- ciency across virtually all domains, this approach enables the generation of high-quality and diversified instructions based on tool
|
2306.05301#26
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 26 |
(i) Correctness of Information: We verify the accuracy of the generated text, ensuring it aligns with the video content and doesnât misinterpret or misinform.
(ii) Detail Orientation: We evaluate the depth of the modelâs responses, looking for both completeness, meaning the modelâs response covers all major points from the video, and specificity, denoting the inclusion of specific details rather than just generic points in the modelâs response.
(iii) Contextual Understanding: We assess the modelâs understanding of the videoâs context, checking if its responses aligns with the overall context of the video content.
(iv) Temporal Understanding: We examine the modelâs grasp of the temporal sequence of events in the video when answering questions.
(v) Consistency: We evaluate the modelâs consistency across different but similar questions or different sections of the video.
|
2306.05424#26
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.05499
| 26 |
Category Business Analysis Chatbot Writing Assistant Code Assistant Creative Generation Target Description DECISIONAI INFOREVOLVE CHATPUBDATA CHATBOTGENIUS COPYWRITERKIT EMAILGENIUS AIWITHUI Decision Making Information Analysis Personalized Chat Personalized Chat Social Media Content Email Writing Web UI Generation AIWORKSPACE Web UI Generation Product Description Product Description STARTGEN STORYCRAFT Direct Injection Q2 â â Q1 â â â (5) â (5) â (5) â (5) â â â â â â â â â â â â Escape Characters Q2 â â Context Ignoring Q2 â â Q3 Q1 â â â â â â â â â â Q3 Q1 â â â â â â â â â â â â â (5) â (5) â (5) â (5) â â â â â â â â â (5) â (5) â (5) â (5) â â â (4) â â â â â â â â
|
2306.05499#26
|
Prompt Injection attack against LLM-integrated Applications
|
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation.
|
http://arxiv.org/pdf/2306.05499
|
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu
|
cs.CR, cs.AI, cs.CL, cs.SE
| null | null |
cs.CR
|
20230608
|
20230608
|
[] |
2306.04926
| 27 |
3. Understanding How COVID-19 Vaccines Work. Centers for Disease Control and Prevention. Published February 3, 2023. Accessed May 2, 2023. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/different- vaccines/how-they-work.html
4. Affairs (ASPA) AS for P. COVID-19 Vaccines. HHS.gov. Published December 12, 2020. Accessed May 2, 2023. https://www.hhs.gov/coronavirus/covid-19-vaccines/index.html
5. COVID-19 Treatments. NIH COVID-19 Research. Accessed May 2, 2023. https://covid19.nih.gov/covid- 19-treatments
6. COVID-19 research update: How many pandemic papers have been published? Nature Index. Published
|
2306.04926#27
|
covLLM: Large Language Models for COVID-19 Biomedical Literature
|
The COVID-19 pandemic led to 1.1 million deaths in the United States, despite
the explosion of coronavirus research. These new findings are slow to translate
to clinical interventions, leading to poorer patient outcomes and unnecessary
deaths. One reason is that clinicians, overwhelmed by patients, struggle to
keep pace with the rate of new coronavirus literature. A potential solution is
developing a tool for evaluating coronavirus literature using large language
models (LLMs) -- neural networks that are deployed for natural language
processing. LLMs can be used to summarize and extract user-specified
information. The greater availability and advancement of LLMs and pre-processed
coronavirus literature databases provide the opportunity to assist clinicians
in evaluating coronavirus literature through a coronavirus literature specific
LLM (covLLM), a tool that directly takes an inputted research article and a
user query to return an answer. Using the COVID-19 Open Research Dataset
(CORD-19), we produced two datasets: (1) synCovid, which uses a combination of
handwritten prompts and synthetic prompts generated using OpenAI, and (2) real
abstracts, which contains abstract and title pairs. covLLM was trained with
LLaMA 7B as a baseline model to produce three models trained on (1) the Alpaca
and synCovid datasets, (2) the synCovid dataset, and (3) the synCovid and real
abstract datasets. These models were evaluated by two human evaluators and
ChatGPT. Results demonstrate that training covLLM on the synCovid and abstract
pairs datasets performs competitively with ChatGPT and outperforms covLLM
trained primarily using the Alpaca dataset.
|
http://arxiv.org/pdf/2306.04926
|
Yousuf A. Khan, Clarisse Hokia, Jennifer Xu, Ben Ehlert
|
cs.CL, cs.AI, cs.LG
| null | null |
cs.CL
|
20230608
|
20230608
|
[] |
2306.05087
| 27 |
Response 2 takes the lead. Note that the human-generated dataset has no personally identifiable information or offensive content, and all annotators receive redundant labor fees.
After obtaining the human-labeled test dataset, we can assess and compare the evaluation perfor- mances of GPT-3.5, GPT-4, and PandaLM-7B. An interesting observation from Table 1 is the shared similar partial order graph between GPT-3.5, GPT-4, PandaLM-7B, and humans. Furthermore, Figure 5 illustrates directed orders of model superiority (if model A outperforms model B, a directed edge from A to B is drawn; if model A and model B perform similarly, a dashed line from A to B is drawn.), and provides a visual representation of comparative model effectiveness. The experimental results indicate similarities in the preferences of GPT-3.5, GPT-4, PandaLM-7B, and humans. Note that for PandaLM, GPT-3.5, and GPT-4, we swap the input response order and infer twice to procure the final evaluation output. The conflicting evaluation results are revised to âTieâ.
|
2306.05087#27
|
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
|
Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishing such a benchmark is not a trivial task due to the challenges
associated with evaluation accuracy and privacy protection. In response to
these challenges, we introduce a judge large language model, named PandaLM,
which is trained to distinguish the superior model given several LLMs.
PandaLM's focus extends beyond just the objective correctness of responses,
which is the main focus of traditional evaluation datasets. It addresses vital
subjective factors such as relative conciseness, clarity, adherence to
instructions, comprehensiveness, and formality. To ensure the reliability of
PandaLM, we collect a diverse human-annotated test dataset, where all contexts
are generated by humans and labels are aligned with human preferences. Our
results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation
ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM
enables the evaluation of LLM to be fairer but with less cost, evidenced by
significant improvements achieved by models tuned through PandaLM compared to
their counterparts trained with default Alpaca's hyperparameters. In addition,
PandaLM does not depend on API-based evaluations, thus avoiding potential data
leakage. All resources of PandaLM are released at
https://github.com/WeOpenML/PandaLM.
|
http://arxiv.org/pdf/2306.05087
|
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang
|
cs.CL, cs.AI
| null | null |
cs.CL
|
20230608
|
20230608
|
[
{
"id": "2302.13971"
},
{
"id": "2204.02311"
},
{
"id": "1803.05457"
},
{
"id": "2305.10403"
},
{
"id": "1807.05118"
},
{
"id": "2211.05100"
},
{
"id": "2302.10198"
},
{
"id": "2205.01068"
},
{
"id": "2003.05689"
},
{
"id": "1806.03822"
},
{
"id": "1711.05101"
},
{
"id": "2304.03208"
},
{
"id": "2304.01373"
},
{
"id": "2303.14742"
},
{
"id": "2303.04673"
},
{
"id": "2212.10560"
},
{
"id": "2211.08073"
},
{
"id": "2210.02414"
},
{
"id": "2304.03277"
},
{
"id": "2002.06305"
},
{
"id": "2305.13412"
},
{
"id": "2304.01196"
}
] |
2306.05152
| 27 |
REFERENCES
[1] M. Shahin, M. Ali Babar, and L. Zhu, âContinu- ous integration, delivery and deployment: A system- atic review on approaches, tools, challenges and prac- tices,â IEEE Access, vol. 5, pp. 3909â3943, 2017. DOI: 10.1109/ACCESS.2017.2685629.
[2] A. Arcuri, âAn experience report on applying software testing academic results in industry: We need usable automated test generation,â Empirical Softw. Engg., vol. 23, no. 4, pp. 1959â1981, Aug. 2018, ISSN: 1382- 3256.
|
2306.05152#27
|
Towards Autonomous Testing Agents via Conversational Large Language Models
|
Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. Recent discoveries of the capabilities of large language models
(LLMs) suggest that they can be used as automated testing assistants, and thus
provide helpful information and even drive the testing process. To highlight
the potential of this technology, we present a taxonomy of LLM-based testing
agents based on their level of autonomy, and describe how a greater level of
autonomy can benefit developers in practice. An example use of LLMs as a
testing assistant is provided to demonstrate how a conversational framework for
testing can help developers. This also highlights how the often criticized
hallucination of LLMs can be beneficial for testing. We identify other tangible
benefits that LLM-driven testing agents can bestow, and also discuss potential
limitations.
|
http://arxiv.org/pdf/2306.05152
|
Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo
|
cs.SE
| null | null |
cs.SE
|
20230608
|
20230905
|
[
{
"id": "2305.10601"
},
{
"id": "2303.17580"
},
{
"id": "2305.16291"
},
{
"id": "2201.09305"
},
{
"id": "2210.03629"
},
{
"id": "2211.10435"
},
{
"id": "2303.12712"
},
{
"id": "2302.03287"
},
{
"id": "2209.11515"
}
] |
2306.05171
| 27 |
Since PlanAssembly needs to decompose more complex tasks into several parts and then continue to decompose, we use a description like "perform an action on object A and object B" as an atomic assembly task to measure the complexity of a given task. We wrote some ideal generations for these two tasks and used five examples from complex to simple to provide the model with context information.
means that âplanAssemblyâ LocateParts may generate subtask PickParts Sequence recursively untill the task is simple ClassifyParts Y enough PlanAssembly IdentifyPart toap iz PlanAssembly | \ + PickPart | â PlacePart CheckPartassembly FinalCheck Reportcomplation
Fig. 3. Schematic of the generation relationships among the tasks in the test cases.
B. Evaluation Method
In order to understand the performance of the proposed method, we use the following indicators for evaluation:
1) Format Success Rate (format_success_rate): It measures whether the skills selected by the model correctly comply with
7
the logical nesting requirements of the instruction set, regardless of whether they are a successful task sequence that can complete the total task at once. It is measured by the ratio of successful formatting times to total generation times.
|
2306.05171#27
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05301
| 27 |
the large modelâs profi- ciency across virtually all domains, this approach enables the generation of high-quality and diversified instructions based on tool documentation. This effectively addresses the previously mentioned issues with manual annotation. ⢠Assistant Agent is designed to simulate an assistant with tool utilization capabilities. It receives instructions from the user agent and determines the subsequent ac- tions. This involves choosing the appropriate tools and functions, generating commands for the tool executor, and summarizing the interaction to generate the final re- sponse. As shown in Figure 3, following ReAct (Yao et al. 2022), we employ a (thought, action, observation) format template to guide LLM in accomplishing these tasks.
|
2306.05301#27
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 27 |
(v) Consistency: We evaluate the modelâs consistency across different but similar questions or different sections of the video.
We present the results of the evaluation of our proposed model, Video-ChatGPT, using the quantitative benchmarking framework in Table 1. The results reveal its competent performance across all key aspects when compared with the recently introduced contemporary video conversation model, Video Chat [8]. Video-ChatGPT shows good performance, largely due to the instruction tuning we perform and its straightforward architecture that leverages LLMs with a pretrained visual encoder fine-tuned for video data. This provides it with the robust ability to generate contextually relevant, detailed, and temporally accurate text from video input.
# vii
Model MSVD-QA MSRVTT-QA TGIF-QA Activity Net-QA Accuracy Score Accuracy Score Accuracy Score Accuracy Score FrozenBiLM Video Chat Video-ChatGPT 32.2 56.3 64.9 â 2.8 3.3 16.8 45.0 49.3 â 2.5 2.8 41.0 34.4 51.4 â 2.3 3.0 24.7 26.5 35.2 â 2.2 2.7
Table 2: Zeroshot question-answering comparison of Video-ChatGPT with other video generative models. Video-ChatGPT performs competitively across all datasets.
|
2306.05424#27
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.04926
| 28 |
6. COVID-19 research update: How many pandemic papers have been published? Nature Index. Published
August 28, 2020. Accessed May 2, 2023. https://www.nature.com/nature-index/news-blog/how- coronavirus-is-changing-research-practices-and-publishing Ioannidis JPA, Salholz-Hillel M, Boyack KW, Baas J. The rapid, massive growth of COVID-19 authors in the scientific literature. R Soc Open Sci. 2021;8(9):210389. doi:10.1098/rsos.210389
8. Park JJH, Mogg R, Smith GE, et al. How COVID-19 has fundamentally changed clinical research in global health. Lancet Glob Health. 2021;9(5):e711-e720. doi:10.1016/S2214-109X(20)30542-8
9. Lee A. What Are Large Language Models and Why Are They Important? NVIDIA Blog. Published January 26, 2023. Accessed May 2, 2023. https://blogs.nvidia.com/blog/2023/01/26/what-are-large- language-models-used-for/
10. The Next Generation Of Large Language Models. Accessed May 2, 2023.
|
2306.04926#28
|
covLLM: Large Language Models for COVID-19 Biomedical Literature
|
The COVID-19 pandemic led to 1.1 million deaths in the United States, despite
the explosion of coronavirus research. These new findings are slow to translate
to clinical interventions, leading to poorer patient outcomes and unnecessary
deaths. One reason is that clinicians, overwhelmed by patients, struggle to
keep pace with the rate of new coronavirus literature. A potential solution is
developing a tool for evaluating coronavirus literature using large language
models (LLMs) -- neural networks that are deployed for natural language
processing. LLMs can be used to summarize and extract user-specified
information. The greater availability and advancement of LLMs and pre-processed
coronavirus literature databases provide the opportunity to assist clinicians
in evaluating coronavirus literature through a coronavirus literature specific
LLM (covLLM), a tool that directly takes an inputted research article and a
user query to return an answer. Using the COVID-19 Open Research Dataset
(CORD-19), we produced two datasets: (1) synCovid, which uses a combination of
handwritten prompts and synthetic prompts generated using OpenAI, and (2) real
abstracts, which contains abstract and title pairs. covLLM was trained with
LLaMA 7B as a baseline model to produce three models trained on (1) the Alpaca
and synCovid datasets, (2) the synCovid dataset, and (3) the synCovid and real
abstract datasets. These models were evaluated by two human evaluators and
ChatGPT. Results demonstrate that training covLLM on the synCovid and abstract
pairs datasets performs competitively with ChatGPT and outperforms covLLM
trained primarily using the Alpaca dataset.
|
http://arxiv.org/pdf/2306.04926
|
Yousuf A. Khan, Clarisse Hokia, Jennifer Xu, Ben Ehlert
|
cs.CL, cs.AI, cs.LG
| null | null |
cs.CL
|
20230608
|
20230608
|
[] |
2306.05087
| 28 |
As shown in Table 2, we conduct a statistical analysis comparing the accuracy, precision, recall, and F1-score of GPT-3.5, GPT-4, and PandaLM-7B against human annotations. GPT-4 demonstrated superior performance, recording the highest scores across all assessed metrics. Despite PandaLM-7B having the lowest F1-score, it still demonstrates a notable performance, achieving 93.75% of GPT- 3.5âs evaluation ability and 88.28% of GPT-4âs in terms of F1-score. Moreover, we are committed to continuously training larger-sized versions of PandaLM to enhance its evaluation performance further.
In addition, beyond performance metrics, PandaLM-7B introduces unique advantages that are not present in models like GPT-3.5 and GPT-4. It offers open-source availability, enabling reproducibility,
7
|
2306.05087#28
|
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
|
Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishing such a benchmark is not a trivial task due to the challenges
associated with evaluation accuracy and privacy protection. In response to
these challenges, we introduce a judge large language model, named PandaLM,
which is trained to distinguish the superior model given several LLMs.
PandaLM's focus extends beyond just the objective correctness of responses,
which is the main focus of traditional evaluation datasets. It addresses vital
subjective factors such as relative conciseness, clarity, adherence to
instructions, comprehensiveness, and formality. To ensure the reliability of
PandaLM, we collect a diverse human-annotated test dataset, where all contexts
are generated by humans and labels are aligned with human preferences. Our
results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation
ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM
enables the evaluation of LLM to be fairer but with less cost, evidenced by
significant improvements achieved by models tuned through PandaLM compared to
their counterparts trained with default Alpaca's hyperparameters. In addition,
PandaLM does not depend on API-based evaluations, thus avoiding potential data
leakage. All resources of PandaLM are released at
https://github.com/WeOpenML/PandaLM.
|
http://arxiv.org/pdf/2306.05087
|
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang
|
cs.CL, cs.AI
| null | null |
cs.CL
|
20230608
|
20230608
|
[
{
"id": "2302.13971"
},
{
"id": "2204.02311"
},
{
"id": "1803.05457"
},
{
"id": "2305.10403"
},
{
"id": "1807.05118"
},
{
"id": "2211.05100"
},
{
"id": "2302.10198"
},
{
"id": "2205.01068"
},
{
"id": "2003.05689"
},
{
"id": "1806.03822"
},
{
"id": "1711.05101"
},
{
"id": "2304.03208"
},
{
"id": "2304.01373"
},
{
"id": "2303.14742"
},
{
"id": "2303.04673"
},
{
"id": "2212.10560"
},
{
"id": "2211.08073"
},
{
"id": "2210.02414"
},
{
"id": "2304.03277"
},
{
"id": "2002.06305"
},
{
"id": "2305.13412"
},
{
"id": "2304.01196"
}
] |
2306.05152
| 28 |
[3] R. Haas, D. Elsner, E. Juergens, A. Pretschner, and S. Apel, âHow can manual testing processes be optimized? developer survey, optimization guidelines, and case studies,â in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineer- ing, ser. ESEC/FSE 2021, Athens, Greece: Association for Computing Machinery, 2021, pp. 1281â1291. [4] M. J. Rutherford and A. L. Wolf, âA case for test-code generation in model-driven systems,â in Proceedings of the 2nd International Conference on Generative Pro- gramming and Component Engineering, 2003, pp. 377â 396. J. T. Liang, M. Arab, M. Ko, A. J. Ko, and T. D. LaToza, A qualitative study on the implemen- tation design decisions of developers, 2023. arXiv: 2301.09789 [cs.SE].
|
2306.05152#28
|
Towards Autonomous Testing Agents via Conversational Large Language Models
|
Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. Recent discoveries of the capabilities of large language models
(LLMs) suggest that they can be used as automated testing assistants, and thus
provide helpful information and even drive the testing process. To highlight
the potential of this technology, we present a taxonomy of LLM-based testing
agents based on their level of autonomy, and describe how a greater level of
autonomy can benefit developers in practice. An example use of LLMs as a
testing assistant is provided to demonstrate how a conversational framework for
testing can help developers. This also highlights how the often criticized
hallucination of LLMs can be beneficial for testing. We identify other tangible
benefits that LLM-driven testing agents can bestow, and also discuss potential
limitations.
|
http://arxiv.org/pdf/2306.05152
|
Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo
|
cs.SE
| null | null |
cs.SE
|
20230608
|
20230905
|
[
{
"id": "2305.10601"
},
{
"id": "2303.17580"
},
{
"id": "2305.16291"
},
{
"id": "2201.09305"
},
{
"id": "2210.03629"
},
{
"id": "2211.10435"
},
{
"id": "2303.12712"
},
{
"id": "2302.03287"
},
{
"id": "2209.11515"
}
] |
2306.05171
| 28 |
2) Parameter Success Rate (parameter_success_rate): For example, if a parameter only accepts one object to be recognized, but the LLM fills in two. We record the number of sub-tasks where all parameters are successfully generated, and take the ratio to the total number of generated sub-tasks.
3) Planning Success Rate (plan_success_rate): It measures whether the generated task sequence successfully executes the total task. As many instructions may have multiple valid solutions simultaneously, we ask 3 human evaluators to judge whether the plan generated by the model can complete the instruction to eliminate this error. If 2 evaluators think the plan is effective, it is marked as successful. It is measured by the ratio of successful times to total generation times.
We examined that require recursive hierarchies, disassembly of assembly steps, and extraction of parameters for single assembly actions.
# V. EXPERIMENT RESULTS
Table. 3. Experimental results for tasks that require recursive layering. MODEL FORMAT_SUCCESS_RATE PARAMETER_SUCCESS_RATE PLAN_SUCCESS_RATE GPT 4. 100% 81.8% 100% GPT 3.5 100% 100% 0%
|
2306.05171#28
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05301
| 28 |
⢠Tool Executor Agent is constructed to emulate the execution of tools, receiving requests from the assis- tant agent and generating responses based on the toolâs predefined functionalities. Specifically, after conduct- ing format and parameter checks on the assistantâs re- quests, these requests are converted into network re- quest formats. Then the tool executor prompts LLM with the toolâs OpenAPI specification and the assistantâs re- quests to generate simulated execution results. Leverag- ing LLMsâ robust simulation and generation capabilities, we mitigate the intricacies involved in constructing actual API calls. This method has been empirically validated as
both accurate and effective, as evidenced in the following section.
Given the above agents, tool-use cases are generated through multiple rounds of interaction between them. Ini- tially, the user agent generates instructions based on the tool information. Subsequently, the assistant agent selects an ap- propriate action and its corresponding input and awaits sim- ulation execution and response from the tool executor. This iterative procedure of action selection and tool response col- lection continues until the assistant agent deems it has gath- ered sufficient information to respond to the userâs instruc- tions. Through this multi-agent interaction, we can simulate realistic tool-use scenarios and generate comprehensive and diversified tool-use instances.
# 4 ToolAlpaca Corpus
# 4.1 Construction Details
|
2306.05301#28
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 28 |
Table 2: Zeroshot question-answering comparison of Video-ChatGPT with other video generative models. Video-ChatGPT performs competitively across all datasets.
Zero-Shot Question-Answer Evaluation: We conducted a comprehensive quantitative evaluation using several commonly used open-ended question-answer datasets: MSRVTT-QA [32], MSVD- QA [32], TGIF-QA FrameQA [33], and ActivityNet-QA [34]. These evaluations were carried out in a zero-shot manner, employing GPT-assisted evaluation to assess the modelâs capabilities. This evaluation process measures the accuracy of the modelâs generated predictions and assigns a relative score on a scale of 1-5.
To benchmark Video-ChatGPT, we compared its performance with other significant models, such as FrozenBiLM [35] and the generative video model, Video Chat. FrozenBiLM is a model that adapts frozen bidirectional language models pretrained on Web-scale text-only data to multi-modal inputs, showing promising results in zero-shot VideoQA settings. Despite the solid foundation established by these models, Video-ChatGPT consistently outperformed them, achieving state-of-the-art (SOTA) performance across all datasets. These results indicate Video-ChatGPTâs ability to understand video content and generate accurate, contextually rich answers to questions.
|
2306.05424#28
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.05499
| 28 |
to deceive an LLM into interpreting the injected prompt as an instruction to be answered separately from the previous context. Our observation from Section 3.2 suggests that, while context-ignoring attacks presented in previous works [4, 20] attempt to create a separation, such approaches have proven insufficient. In particular, a simple prompt of âignore the previous contextâ often gets overshadowed by larger, task- specific contexts, thus not powerful enough to isolate the malicious question. Moreover, these approaches do not take into account the previous context. In parallel with traditional injection attacks, it appears that they employ an unsuitable payload for achieving this separation.
with the defined format. ⸠In the next step, HOUYI creates a separator prompt, which disrupts the semantic connection between the previous context and the adversarial question. By summarizing effective strategies from our pilot study and combining them with the inferred context, it generates a sep- arator prompt customized for the target application. â¹ The last component of the injected prompt involves creating a disruptor component that houses the adversaryâs malicious intent. While the intent can be straightforward, we provide several tricks to encode this prompt for a higher success rate. These three components are then merged into one prompt and input into the application for response generation.
|
2306.05499#28
|
Prompt Injection attack against LLM-integrated Applications
|
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation.
|
http://arxiv.org/pdf/2306.05499
|
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu
|
cs.CR, cs.AI, cs.CL, cs.SE
| null | null |
cs.CR
|
20230608
|
20230608
|
[] |
2306.04926
| 29 |
10. The Next Generation Of Large Language Models. Accessed May 2, 2023.
https://www.forbes.com/sites/robtoews/2023/02/07/the-next-generation-of-large-language- models/?sh=6e32ff9d18db
11. Introducing LLaMA: A foundational, 65-billion-parameter language model. Accessed May 2, 2023. https://ai.facebook.com/blog/large-language-model-llama-meta-ai/
12. Stanford CRFM. Accessed May 2, 2023. https://crfm.stanford.edu/2023/03/13/alpaca.html 13. How the Google AI Community Used Cloud to Help Biomedical Researchers. Google Cloud Blog.
Accessed May 2, 2023. https://cloud.google.com/blog/products/ai-machine-learning/google-ai-community- used-cloud-to-help-biomedical-researchers
14. Lever J, Altman RB. Analyzing the vast coronavirus literature with CoronaCentral. Proc Natl Acad Sci. 2021;118(23):e2100766118. doi:10.1073/pnas.2100766118
|
2306.04926#29
|
covLLM: Large Language Models for COVID-19 Biomedical Literature
|
The COVID-19 pandemic led to 1.1 million deaths in the United States, despite
the explosion of coronavirus research. These new findings are slow to translate
to clinical interventions, leading to poorer patient outcomes and unnecessary
deaths. One reason is that clinicians, overwhelmed by patients, struggle to
keep pace with the rate of new coronavirus literature. A potential solution is
developing a tool for evaluating coronavirus literature using large language
models (LLMs) -- neural networks that are deployed for natural language
processing. LLMs can be used to summarize and extract user-specified
information. The greater availability and advancement of LLMs and pre-processed
coronavirus literature databases provide the opportunity to assist clinicians
in evaluating coronavirus literature through a coronavirus literature specific
LLM (covLLM), a tool that directly takes an inputted research article and a
user query to return an answer. Using the COVID-19 Open Research Dataset
(CORD-19), we produced two datasets: (1) synCovid, which uses a combination of
handwritten prompts and synthetic prompts generated using OpenAI, and (2) real
abstracts, which contains abstract and title pairs. covLLM was trained with
LLaMA 7B as a baseline model to produce three models trained on (1) the Alpaca
and synCovid datasets, (2) the synCovid dataset, and (3) the synCovid and real
abstract datasets. These models were evaluated by two human evaluators and
ChatGPT. Results demonstrate that training covLLM on the synCovid and abstract
pairs datasets performs competitively with ChatGPT and outperforms covLLM
trained primarily using the Alpaca dataset.
|
http://arxiv.org/pdf/2306.04926
|
Yousuf A. Khan, Clarisse Hokia, Jennifer Xu, Ben Ehlert
|
cs.CL, cs.AI, cs.LG
| null | null |
cs.CL
|
20230608
|
20230608
|
[] |
2306.05087
| 29 |
7
Table 3: Evaluation of the effectiveness of PandaLMâs selected hyperparameters and Alpacaâs hyperparameters. The tuple in the table means (#win,#lose,#tie). Specifically, (45,26,99) in the first line of the table indicates that PandaLMâs hyperparameter-tuned LLaMA-7B outperforms Alpacaâs version in 45 responses, underperforms in 26, and matches the quality in 99 instances. The âJudged Byâ column represents different methods of response evaluation. Judge Model LLaMA-7B Bloom-7B Cerebras-6.7B
OPT-7B Pythia-6.9B GPT-3.5 GPT-4 Human (45,26,99) (40,17,113) (82,21,67) (48,24,98) (44,34,92) (79,23,68) (58,21,91) (60,20,90) (88,25,57) (48,34,88) (39,30,101) (68,26,76) (59,20,91) (52,30,88) (82,31,57)
and protecting data privacy. Furthermore, it provides unlimited access, removing any restrictions that might hinder comprehensive evaluation and application.
|
2306.05087#29
|
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
|
Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishing such a benchmark is not a trivial task due to the challenges
associated with evaluation accuracy and privacy protection. In response to
these challenges, we introduce a judge large language model, named PandaLM,
which is trained to distinguish the superior model given several LLMs.
PandaLM's focus extends beyond just the objective correctness of responses,
which is the main focus of traditional evaluation datasets. It addresses vital
subjective factors such as relative conciseness, clarity, adherence to
instructions, comprehensiveness, and formality. To ensure the reliability of
PandaLM, we collect a diverse human-annotated test dataset, where all contexts
are generated by humans and labels are aligned with human preferences. Our
results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation
ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM
enables the evaluation of LLM to be fairer but with less cost, evidenced by
significant improvements achieved by models tuned through PandaLM compared to
their counterparts trained with default Alpaca's hyperparameters. In addition,
PandaLM does not depend on API-based evaluations, thus avoiding potential data
leakage. All resources of PandaLM are released at
https://github.com/WeOpenML/PandaLM.
|
http://arxiv.org/pdf/2306.05087
|
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang
|
cs.CL, cs.AI
| null | null |
cs.CL
|
20230608
|
20230608
|
[
{
"id": "2302.13971"
},
{
"id": "2204.02311"
},
{
"id": "1803.05457"
},
{
"id": "2305.10403"
},
{
"id": "1807.05118"
},
{
"id": "2211.05100"
},
{
"id": "2302.10198"
},
{
"id": "2205.01068"
},
{
"id": "2003.05689"
},
{
"id": "1806.03822"
},
{
"id": "1711.05101"
},
{
"id": "2304.03208"
},
{
"id": "2304.01373"
},
{
"id": "2303.14742"
},
{
"id": "2303.04673"
},
{
"id": "2212.10560"
},
{
"id": "2211.08073"
},
{
"id": "2210.02414"
},
{
"id": "2304.03277"
},
{
"id": "2002.06305"
},
{
"id": "2305.13412"
},
{
"id": "2304.01196"
}
] |
2306.05152
| 29 |
[6] R. Feldt, S. Poulding, D. Clark, and S. Yoo, âTest set diameter: Quantifying the diversity of sets of test cases,â in Proceedings of the IEEE International Conference on Software Testing, Veriï¬cation, and Validation, ser. ICST 2016, 2016, pp. 223â233.
[7] T. Y. Chen, F.-C. Kuo, R. G. Merkel, and T. Tse, âAdaptive random testing: The art of test case diver- sity,â Journal of Systems and Software, vol. 83, no. 1, pp. 60â66, 2010.
[8] L. Gao, S. Biderman, S. Black, et al., The pile: An 800gb dataset of diverse text for language modeling, 2020. arXiv: 2101.00027 [cs.CL].
[9] S. Jalil, S. Raï¬, T. D. LaToza, K. Moran, and W. Lam, âChatgpt and software testing education: Promises & perils,â arXiv preprint arXiv:2302.03287, 2023. technical
[10] OpenAI, Gpt-4 report, 2023. arXiv: 2303.08774 [cs.CL].
|
2306.05152#29
|
Towards Autonomous Testing Agents via Conversational Large Language Models
|
Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. Recent discoveries of the capabilities of large language models
(LLMs) suggest that they can be used as automated testing assistants, and thus
provide helpful information and even drive the testing process. To highlight
the potential of this technology, we present a taxonomy of LLM-based testing
agents based on their level of autonomy, and describe how a greater level of
autonomy can benefit developers in practice. An example use of LLMs as a
testing assistant is provided to demonstrate how a conversational framework for
testing can help developers. This also highlights how the often criticized
hallucination of LLMs can be beneficial for testing. We identify other tangible
benefits that LLM-driven testing agents can bestow, and also discuss potential
limitations.
|
http://arxiv.org/pdf/2306.05152
|
Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo
|
cs.SE
| null | null |
cs.SE
|
20230608
|
20230905
|
[
{
"id": "2305.10601"
},
{
"id": "2303.17580"
},
{
"id": "2305.16291"
},
{
"id": "2201.09305"
},
{
"id": "2210.03629"
},
{
"id": "2211.10435"
},
{
"id": "2303.12712"
},
{
"id": "2302.03287"
},
{
"id": "2209.11515"
}
] |
2306.05171
| 29 |
layering. MODEL _FORMAT_SUCCESS_RATE _PARAMETER_SUCCESS_RATE _PLAN_SUCCESS_RATE â100% 81.8% 100% 100% 100% 0% GPT 4. GPT 3.5
Table. 4. Experimental results for tasks that disassembly step decomposition tasks. MODEL FORMAT_SUCCESS_RATE PARAMETER_SUCCESS_RATE PLAN_SUCCESS_RATE GPT 4. 100% 79.2% 83.3% GPT 3.5 100% 66.6% 83.3%
decomposition MODEL _FORMAT_SUCCESS_RATE PARAMETER SUCCESS RATE PLAN SUCCESS RATE GPT 4. GPT 3.5 100% 100% 79.2% 66.6% 83.3% 83.3%
Table. 5. Experimental results for tasks that single assembly action parameter extraction tasks. MODEL FORMAT_SUCCESS_RATE PARAMETER_SUCCESS_RATE PLAN_SUCCESS_RATE GPT 4. 100% 100% 100% GPT 3.5 100% 100% 100%
RATE PARAMETER SUC! MODEL FORMAT_SU GPT 4. 100% GPT 3.5 100% 100% 100%
|
2306.05171#29
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05301
| 29 |
# 4 ToolAlpaca Corpus
# 4.1 Construction Details
Leveraging the aforementioned multi-agent simulation framework, we have constructed the ToolAlpaca corpus. Specifically, the process begins with randomly selecting 500 APIs from the public-apis repository. Subsequently, we uti- lize ChatGPT to generate more comprehensive documenta- tion, resulting in a varied and well-structured toolset. Within our simulation environment, we use ChatGPT as the user agent to generate ten instructions for each tool, and the tool executor to simulate tool execution. We appoint GPT-3.5 as the assistant agent due to its superior performance in struc- tured output generation.3
To guarantee the quality of the data, we implement a sim- ple yet effective filtering process on the generated corpus. We systematically exclude tools that might incorporate non- textual content within their inputs or outputs. In terms of in- stances, we discard those that exceed five interaction steps, lack relevant function calls, or exhibit parsing errors in their output.
Finally, we automatically construct an extensive and di- versified tool-use corpus. As shown in Table 1, it encom- passes 426 distinctive tools from 50 categories, totaling 3938 instances. In the following sections, we will analyze the di- versity and quality of our corpus.
|
2306.05301#29
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 29 |
# 5.3 Qualitative Evaluation
We performed an extensive evaluation of our model on a variety of open-ended video question- answering tasks, utilizing diverse videos sourced from ActivityNet and YouTube. The evaluation tasks included video reasoning (Figure 4), creative and generative tasks (see Figure 5), spatial understanding (Figure 6), action recognition (Figure 7), video conversation (Figure 8), question answering (Figure 9) and temporal understanding (Figure 10). Our model demonstrates proficiency in comprehending the content of the videos and generating accurate responses across multiple video based task. Our model can effectively understand the visual information present in the videos and provide precise answers (see Figures 4 to 10).
# 6 Conclusion and Future Directions
|
2306.05424#29
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.05499
| 29 |
Our key insight is the necessity of an appropriate separator component, a construct based on the preceding context to effectively isolate the malicious command. The challenge lies in designing malicious prompts that not only mimic legitimate commands convincingly to deceive the LLM, but also embed the malicious command effectively. Consequently, this would bypass any pre-established context shaped by the applicationâs pre-designed prompts.
# 4.2 Attack Workflow
Prompt Refinement with Dynamic Feedback. Once the application generates a response, ⺠HOUYI dynamically as- sesses it using a custom LLM (e.g., GPT-3.5). This dynamic analysis helps to discern whether the prompt injection has successfully exploited the application, or if alterations to the injection strategy are necessary. This feedback process evalu- ates the relevance of the response to the adversaryâs intent, the format alignment with expected output, and any other notable patterns. Based on the evaluation, the Separator and Disruptor Components of the injection prompt may undergo iterative modifications to enhance the effectiveness of the attack.
|
2306.05499#29
|
Prompt Injection attack against LLM-integrated Applications
|
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation.
|
http://arxiv.org/pdf/2306.05499
|
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu
|
cs.CR, cs.AI, cs.CL, cs.SE
| null | null |
cs.CR
|
20230608
|
20230608
|
[] |
2306.04926
| 30 |
15. BioMedLM: a Domain-Specific Large Language Model for Biomedical Text. Accessed May 2, 2023. https://www.mosaicml.com/blog/introducing-pubmed-gpt
16. BREATHE BioMedical Literature Dataset â Marketplace â Google Cloud console. Accessed May 2, 2023. https://console.cloud.google.com/marketplace/product/breathe-gcp-public-data/breathe?hl=da- GL&project=bmi-212
17. Wang LL, Lo K, Chandrasekhar Y, et al. CORD-19: The COVID-19 Open Research Dataset. Published online July 10, 2020. Accessed May 2, 2023. http://arxiv.org/abs/2004.10706
18. Wang Y, Kordi Y, Mishra S, et al. Self-Instruct: Aligning Language Model with Self Generated
Instructions. Published online December 20, 2022. Accessed May 2, 2023. http://arxiv.org/abs/2212.10560 19. Stanford Alpaca: An Instruction-following LLaMA Model. Published online May 2, 2023. Accessed May
2, 2023. https://github.com/tatsu-lab/stanford_alpaca
|
2306.04926#30
|
covLLM: Large Language Models for COVID-19 Biomedical Literature
|
The COVID-19 pandemic led to 1.1 million deaths in the United States, despite
the explosion of coronavirus research. These new findings are slow to translate
to clinical interventions, leading to poorer patient outcomes and unnecessary
deaths. One reason is that clinicians, overwhelmed by patients, struggle to
keep pace with the rate of new coronavirus literature. A potential solution is
developing a tool for evaluating coronavirus literature using large language
models (LLMs) -- neural networks that are deployed for natural language
processing. LLMs can be used to summarize and extract user-specified
information. The greater availability and advancement of LLMs and pre-processed
coronavirus literature databases provide the opportunity to assist clinicians
in evaluating coronavirus literature through a coronavirus literature specific
LLM (covLLM), a tool that directly takes an inputted research article and a
user query to return an answer. Using the COVID-19 Open Research Dataset
(CORD-19), we produced two datasets: (1) synCovid, which uses a combination of
handwritten prompts and synthetic prompts generated using OpenAI, and (2) real
abstracts, which contains abstract and title pairs. covLLM was trained with
LLaMA 7B as a baseline model to produce three models trained on (1) the Alpaca
and synCovid datasets, (2) the synCovid dataset, and (3) the synCovid and real
abstract datasets. These models were evaluated by two human evaluators and
ChatGPT. Results demonstrate that training covLLM on the synCovid and abstract
pairs datasets performs competitively with ChatGPT and outperforms covLLM
trained primarily using the Alpaca dataset.
|
http://arxiv.org/pdf/2306.04926
|
Yousuf A. Khan, Clarisse Hokia, Jennifer Xu, Ben Ehlert
|
cs.CL, cs.AI, cs.LG
| null | null |
cs.CL
|
20230608
|
20230608
|
[] |
2306.05152
| 30 |
[10] OpenAI, Gpt-4 report, 2023. arXiv: 2303.08774 [cs.CL].
[11] AutoGPT: periment, https://github.com/Signiï¬cant-Gravitas/Auto-GPT. J. R. Anderson, How can the human mind occur in the physical universe? Oxford University Press, 2009. J. E. Laird, The SOAR cognitive architecture. MIT press, 2019. J. E. Laird, âAn analysis and comparison of act-r and soar,â arXiv preprint arXiv:2201.09305, 2022.
J. S. Park, J. C. OâBrien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein, Generative agents: Interactive simulacra of human behavior, 2023. arXiv: 2304.03442 [cs.HC].
[16] G. Wang, Y. Xie, Y. Jiang, et al., âVoyager: An open- ended embodied agent with large language models,â arXiv preprint arXiv:2305.16291, 2023.
|
2306.05152#30
|
Towards Autonomous Testing Agents via Conversational Large Language Models
|
Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. Recent discoveries of the capabilities of large language models
(LLMs) suggest that they can be used as automated testing assistants, and thus
provide helpful information and even drive the testing process. To highlight
the potential of this technology, we present a taxonomy of LLM-based testing
agents based on their level of autonomy, and describe how a greater level of
autonomy can benefit developers in practice. An example use of LLMs as a
testing assistant is provided to demonstrate how a conversational framework for
testing can help developers. This also highlights how the often criticized
hallucination of LLMs can be beneficial for testing. We identify other tangible
benefits that LLM-driven testing agents can bestow, and also discuss potential
limitations.
|
http://arxiv.org/pdf/2306.05152
|
Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo
|
cs.SE
| null | null |
cs.SE
|
20230608
|
20230905
|
[
{
"id": "2305.10601"
},
{
"id": "2303.17580"
},
{
"id": "2305.16291"
},
{
"id": "2201.09305"
},
{
"id": "2210.03629"
},
{
"id": "2211.10435"
},
{
"id": "2303.12712"
},
{
"id": "2302.03287"
},
{
"id": "2209.11515"
}
] |
2306.05171
| 30 |
RATE PARAMETER SUC! MODEL FORMAT_SU GPT 4. 100% GPT 3.5 100% 100% 100%
# VI. RESULT ANALYSIS
A. Feasible Parts
1) Stably generates the specified code format; 2) Understands the relationship between tasks and sub-tasks, possible sub-task sequences, and generates sub-task sequences according to the requirements of possible sub-tasks; 3) Understands the meaning of parameters and extracts parameters from text descriptions; 4) Understands the logic of recursive tasks and follows the task decomposition structure.
B. Existing Problems
1) The logic complexity of processing tasks at a single time is limited;
2) There is ambiguity in the grasp of the number of parts and the precise location of assembly; 3) Errors in parameter extraction occur when the concept of "parts" is unclear, which generally happens when there are other references to this part in the task description. For example, "metal rod" and "drive shaft" refer to the same object in context; 4) It should be noted that the language comprehension ability of GPT3.5 seems not to support it to understand a requirement to recursively decompose tasks. We tried to change its behavior in the interaction, but it was completely unsuccessful.
C. Possible Solutions
In response to the problems encountered, we have attempted to propose some possible solutions:
|
2306.05171#30
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05301
| 30 |
statistics # of Tool Categories # of Tools # of Instance # of single function call # of multiple function calls avg. functions per tool avg. steps avg. instruction length avg. output length 50 426 3, 938 2, 512 1, 426 4.85 1.66 23.42 36.19
Table 1: Statistics of ToolAlpaca corpus.
3Preliminary experiments demonstrated an occasional inability of ChatGPT to strictly adhere to the prescribed output formats.
# Instance vw Sa $se ° oF 10 20 30 40 50 60 Instruction Length 90 60 30 # Instance ° R 8 40 60 80 100 120 Output Length
Figure 4: Length distribution of generated instructions and final outputs.
4.2 Diversity As previously underscored, diversity is pivotal for large models to acquire generalized capabilities and adapt to a multitude of unseen scenarios (Wang et al. 2023). ToolAl- paca corpus demonstrates diversity in two aspects:
|
2306.05301#30
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 30 |
# 6 Conclusion and Future Directions
In this work, we presented Video-ChatGPT, a multimodal model that merges a pretrained visual encoder with a large language model (LLM) to enable video understanding and conversations based on videos. Video-ChatGPT leverages an adapter on top of pretrained LLM and vision backbones and is fine-tuned on video-instruction data to capture temporal dynamics and spatial consistency relationships in spatiotemporal sequences. A dataset of 100,000 video-instruction pairs is created to enhance Video-ChatGPTâs video-specific understanding and conversation capabilities. The work also introduced a quantitative video conversation evaluation framework for benchmarking, evaluating models on a diverse set of capabilities including conventional video question answering as well as open-ended descriptions. While the model performs competitively in several scenarios, we note it finds it challenging to understand subtle temporal relationships and the visual details of small objects. As a future work, Video-ChatGPT can be extended to simultaneously deal with multiple modalities and to enhance its video comprehension capabilities towards an all-in-one dialogue agent for universal visual content understanding.
# Acknowledgements
|
2306.05424#30
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.05499
| 30 |
Drawing upon our design rationale, we propose HOUYI, a novel prompt injection attack methodology tailored for LLM- integrated applications in black-box scenarios. Figure 4 pro- vides an outline of HOUYI. We leverage the power of an LLM with custom prompts to analyze the target application and generate the prompt injection attack. HOUYI only requires ap- propriate access to the target LLM-integrated application and its documentation, without further knowledge to the internal system. The workflow contains the following key steps.
HOUYI recursively executes the above steps, continually refining its approach based on the dynamic feedback. Ulti- mately, it outputs a collection of successful attack prompts. We detail the workflow of HOUYI in Section 5.
# 5 Methodology Details
# 5.1 Prompt Composition
Application Context Inference. ⶠHOUYI starts with inferring the internal context created by the applicationâs pre- designed prompts. This process interacts with the target ap- plication as per its usage examples and documentation, then analyzes the resulting input-output pairs using a custom LLM to infer the context within the application.
|
2306.05499#30
|
Prompt Injection attack against LLM-integrated Applications
|
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation.
|
http://arxiv.org/pdf/2306.05499
|
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu
|
cs.CR, cs.AI, cs.CL, cs.SE
| null | null |
cs.CR
|
20230608
|
20230608
|
[] |
2306.04926
| 31 |
2, 2023. https://github.com/tatsu-lab/stanford_alpaca
20. Zhou C, Liu P, Xu P, et al. LIMA: Less Is More for Alignment. Published online May 18, 2023. doi:10.48550/arXiv.2305.11206
21. Touvron H, Lavril T, Izacard G, et al. LLaMA: Open and Efficient Foundation Language Models. Published online February 27, 2023. Accessed May 2, 2023. http://arxiv.org/abs/2302.13971
22. Hu EJ, Shen Y, Wallis P, et al. LoRA: Low-Rank Adaptation of Large Language Models. Published online October 16, 2021. doi:10.48550/arXiv.2106.09685
23. Wang EJ. ð¤ Alpaca-LoRA. Published online June 7, 2023. Accessed June 7, 2023. https://github.com/tloen/alpaca-lora
|
2306.04926#31
|
covLLM: Large Language Models for COVID-19 Biomedical Literature
|
The COVID-19 pandemic led to 1.1 million deaths in the United States, despite
the explosion of coronavirus research. These new findings are slow to translate
to clinical interventions, leading to poorer patient outcomes and unnecessary
deaths. One reason is that clinicians, overwhelmed by patients, struggle to
keep pace with the rate of new coronavirus literature. A potential solution is
developing a tool for evaluating coronavirus literature using large language
models (LLMs) -- neural networks that are deployed for natural language
processing. LLMs can be used to summarize and extract user-specified
information. The greater availability and advancement of LLMs and pre-processed
coronavirus literature databases provide the opportunity to assist clinicians
in evaluating coronavirus literature through a coronavirus literature specific
LLM (covLLM), a tool that directly takes an inputted research article and a
user query to return an answer. Using the COVID-19 Open Research Dataset
(CORD-19), we produced two datasets: (1) synCovid, which uses a combination of
handwritten prompts and synthetic prompts generated using OpenAI, and (2) real
abstracts, which contains abstract and title pairs. covLLM was trained with
LLaMA 7B as a baseline model to produce three models trained on (1) the Alpaca
and synCovid datasets, (2) the synCovid dataset, and (3) the synCovid and real
abstract datasets. These models were evaluated by two human evaluators and
ChatGPT. Results demonstrate that training covLLM on the synCovid and abstract
pairs datasets performs competitively with ChatGPT and outperforms covLLM
trained primarily using the Alpaca dataset.
|
http://arxiv.org/pdf/2306.04926
|
Yousuf A. Khan, Clarisse Hokia, Jennifer Xu, Ben Ehlert
|
cs.CL, cs.AI, cs.LG
| null | null |
cs.CL
|
20230608
|
20230608
|
[] |
2306.05087
| 31 |
To highlight the effectiveness of using PandaLM-7B for instruction tuning LLMs, we compare the performance of models tuned with PandaLMâs selected optimal hyperparameters against those tuned with Alpacaâs parameters using GPT-3.5, GPT-4, and human experts. This comparison evaluates multiple tuned LLMs: LLaMA-7B, Bloom-7B, Cerebras-GPT-6.7B, OPT-7B, and Pythia-6.9B. The assessment is conducted on a validation set comprising 170 distinct instructions and inputs obtained from our 1K test set introduced in Section 4. Alpacaâs tuning protocol involves training for three epochs with the final iterationâs checkpoints being used. It uses the AdamW [52] optimizer with a learning rate of 2e-5 and a cosine learning rate scheduler. We perform a wider range of hyperparamters to tune LLMs using PandaLM-7B. Specifically, we explore checkpoints from each epoch (ranging from epoch 1 to epoch 5), four different learning rates (2e-6, 1e-5, 2e-5, 2e-4), two types of optimizers (SGD[54] and AdamW), and two learning rate schedulers (cosine and linear). In total, this creates a configuration space of 80 different possibilities per model.
|
2306.05087#31
|
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
|
Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishing such a benchmark is not a trivial task due to the challenges
associated with evaluation accuracy and privacy protection. In response to
these challenges, we introduce a judge large language model, named PandaLM,
which is trained to distinguish the superior model given several LLMs.
PandaLM's focus extends beyond just the objective correctness of responses,
which is the main focus of traditional evaluation datasets. It addresses vital
subjective factors such as relative conciseness, clarity, adherence to
instructions, comprehensiveness, and formality. To ensure the reliability of
PandaLM, we collect a diverse human-annotated test dataset, where all contexts
are generated by humans and labels are aligned with human preferences. Our
results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation
ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM
enables the evaluation of LLM to be fairer but with less cost, evidenced by
significant improvements achieved by models tuned through PandaLM compared to
their counterparts trained with default Alpaca's hyperparameters. In addition,
PandaLM does not depend on API-based evaluations, thus avoiding potential data
leakage. All resources of PandaLM are released at
https://github.com/WeOpenML/PandaLM.
|
http://arxiv.org/pdf/2306.05087
|
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang
|
cs.CL, cs.AI
| null | null |
cs.CL
|
20230608
|
20230608
|
[
{
"id": "2302.13971"
},
{
"id": "2204.02311"
},
{
"id": "1803.05457"
},
{
"id": "2305.10403"
},
{
"id": "1807.05118"
},
{
"id": "2211.05100"
},
{
"id": "2302.10198"
},
{
"id": "2205.01068"
},
{
"id": "2003.05689"
},
{
"id": "1806.03822"
},
{
"id": "1711.05101"
},
{
"id": "2304.03208"
},
{
"id": "2304.01373"
},
{
"id": "2303.14742"
},
{
"id": "2303.04673"
},
{
"id": "2212.10560"
},
{
"id": "2211.08073"
},
{
"id": "2210.02414"
},
{
"id": "2304.03277"
},
{
"id": "2002.06305"
},
{
"id": "2305.13412"
},
{
"id": "2304.01196"
}
] |
2306.05152
| 31 |
[17] S. Kang, J. Yoon, and S. Yoo, âLarge language models are few-shot testers: Exploring llm-based general bug reproduction,â arXiv preprint arXiv:2209.11515, 2022. [18] G. Fraser and A. Zeller, âMutation-driven generation of unit tests and oracles,â in Proceedings of the ACM Inter- national Symposium on Software Testing and Analysis, 2010, pp. 147â158. DOI: 10.1145/1831708.1831728. JaCoCo Java Code Coverage Library, 2013. [Online]. Available: https://github.com/jacoco/jacoco.
[20] H. Chase, Langchain, 2022. [Online]. Available:
https://github.com/hwchase17/langchain. J. Bezanson, S. Karpinski, V. B. Shah, and A. Edelman, âJulia: A fast dynamic language for technical comput- ing,â arXiv preprint arXiv:1209.5145, 2012.
[21]
|
2306.05152#31
|
Towards Autonomous Testing Agents via Conversational Large Language Models
|
Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. Recent discoveries of the capabilities of large language models
(LLMs) suggest that they can be used as automated testing assistants, and thus
provide helpful information and even drive the testing process. To highlight
the potential of this technology, we present a taxonomy of LLM-based testing
agents based on their level of autonomy, and describe how a greater level of
autonomy can benefit developers in practice. An example use of LLMs as a
testing assistant is provided to demonstrate how a conversational framework for
testing can help developers. This also highlights how the often criticized
hallucination of LLMs can be beneficial for testing. We identify other tangible
benefits that LLM-driven testing agents can bestow, and also discuss potential
limitations.
|
http://arxiv.org/pdf/2306.05152
|
Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo
|
cs.SE
| null | null |
cs.SE
|
20230608
|
20230905
|
[
{
"id": "2305.10601"
},
{
"id": "2303.17580"
},
{
"id": "2305.16291"
},
{
"id": "2201.09305"
},
{
"id": "2210.03629"
},
{
"id": "2211.10435"
},
{
"id": "2303.12712"
},
{
"id": "2302.03287"
},
{
"id": "2209.11515"
}
] |
2306.05171
| 31 |
C. Possible Solutions
In response to the problems encountered, we have attempted to propose some possible solutions:
1) Improve the precision of task description; 2) Enhance the precision of thinking structure: a. Precise description of the task; b. Increase the depth of task decomposition and control the
amount of tasks handled at one time.
Adjusting in these aspects can have some effects. Thanks to the convenience that the LLM can change its behavior based on text prompts, just by adding constraint instructions in the task description and providing it with more Prompts, the generation results can be improved to a certain extent.
# VII. CONLUSION
By integrating professional knowledge into the language model, we can enable it to solve professional tasks. This will allow the language model to understand and generate task planning schemes related to professional fields, providing guidance and decision support for robots or other intelligent systems. For example, in the medical field, the language model can generate reasonable diagnosis and treatment plans based on medical knowledge and guidelines; in the financial field, the language model can generate optimized investment portfolio planning based on market analysis and investment strategies. Combining the language model with the thinking framework of professional fields can not only improve the accuracy and efficiency of task planning, but also reduce the involvement of human experts. The language model can become a powerful intelligent assistant, providing real-time decision support and task planning suggestions for professionals.
|
2306.05171#31
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05301
| 31 |
⢠Toolset. As outlined in Table 1, our toolset demonstrates diversity in multiple aspects: (1) The toolset encom- passes 50 categories of tools, ranging from common cat- egories, such as jobs and news, to specialized categories like blockchain and finance. (2) Each tool provides an average of five functions, highlighting the diversity and comprehensiveness of its capabilities. (3) The range of function inputs varies from simple to complex scenarios, including arrays and objects, further enhancing the rich- ness and complexity of our toolset.
⢠Instances. The instances within the ToolAlpaca corpus demonstrate diversity in terms of instruction, function calls, and error handling. Specifically, we employ a vari- ety of prompts during instruction generation to stimulate the language model in producing diverse instructions. The wide-ranging distribution of instruction length, as il- lustrated in Figure 4, partly substantiates this point. Addi- tionally, our dataset contains about 1.5k instances that re- quire multiple function invocations for resolution, further underscoring the comprehensiveness of our dataset. Fur- thermore, our data adequately reflects the potential errors that may be encountered in authentic tool usage scenar- ios, encompassing instances that involve various types of errors, such as invalid actions, parsing errors, and incor- rect parameters.
|
2306.05301#31
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 31 |
# Acknowledgements
We would like to thank colleagues for their contribution to the video annotation task, including Abdelrahman Shaker, Shahina Kunhimon, Muhammad Uzair, Sanoojan Baliah, Malitha Gunaward- hana, Akhtar Munir, Vishal Thengane, Vignagajan Vigneswaran, Jiale Cao, Nian Liu, Muhammad Ali, Gayal Kurrupu, Roba Al Majzoub, Jameel Hassan, Hanan Ghani, Muzammal Naseer, Akshay Dudhane, Jean Lahoud, Awais Rauf, Sahal Shaji, Bokang Jia.
viii
|
2306.05424#31
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.05499
| 31 |
Injection Prompt Generation. With the context known, the injection prompt, consisting of three parts, is then gener- ated. â· HOUYI formulates a framework prompt to simulate normal interaction with the application. This step is vital as direct prompt injection can be easily detected if the generated results do not relate to the applicationâs purpose or comply
We use three components to form the injected prompt, each component serving a specific purpose to complete the attack. 1. Framework Component: This component resembles a prompt that naturally aligns with the applicationâs flow, making the malicious injection less detectable. An under- standing of the applicationâs context and conversation flow is required to design this component. In practice, many applications only display content that adheres to pre-set formats. Adding a Framework Component can help to bypass such detection.
2. Separator Component: This component initiates a con- text separation between the pre-set prompts and user in6
Context Inferrer Custom LLM Multiple Execution Key LLM -integrated App {2 Sample Responses (6 Target LLM : @ Injection Responses Function
Figure 4: Overview of HOUYI.
|
2306.05499#31
|
Prompt Injection attack against LLM-integrated Applications
|
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation.
|
http://arxiv.org/pdf/2306.05499
|
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu
|
cs.CR, cs.AI, cs.CL, cs.SE
| null | null |
cs.CR
|
20230608
|
20230608
|
[] |
2306.05087
| 32 |
We search for optimal hyperparameters among the 80 configurations. These are divided into four blocks, each containing 20 configurations. Sequential comparisons identify the best configuration in each block. The top configurations from each block are then compared to determine the overall best configuration. We repeat each comparison twice for robustness and carry out 800 comparisons in total. The conflicting evaluation results are modified to âTieâ. Key insights from our tuning process include: Bloom-7B performs best with SGD, a learning rate of 2e-5, and a cosine schedule over 5 epochs. Cerebras-GPT-6.7B also favors SGD with the same learning rate but with a linear schedule. LLaMA-7B prefers AdamW, a learning rate of 1e-5, and a linear schedule over 4 epochs. OPT-6.7B achieves top results with AdamW, a learning rate of 2e-5, and a linear scheduler over 5 epochs. Pythia-6.9B prefers SGD, a learning rate of 1e-5, a cosine schedule, and 5 epochs. This highlights the importance of customized hyperparameter tuning for different models to achieve peak performance. We also provide the analysis on data size and quality and LoRA when instruction tuning LLMs in Appendix B and Appedix C.
|
2306.05087#32
|
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
|
Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishing such a benchmark is not a trivial task due to the challenges
associated with evaluation accuracy and privacy protection. In response to
these challenges, we introduce a judge large language model, named PandaLM,
which is trained to distinguish the superior model given several LLMs.
PandaLM's focus extends beyond just the objective correctness of responses,
which is the main focus of traditional evaluation datasets. It addresses vital
subjective factors such as relative conciseness, clarity, adherence to
instructions, comprehensiveness, and formality. To ensure the reliability of
PandaLM, we collect a diverse human-annotated test dataset, where all contexts
are generated by humans and labels are aligned with human preferences. Our
results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation
ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM
enables the evaluation of LLM to be fairer but with less cost, evidenced by
significant improvements achieved by models tuned through PandaLM compared to
their counterparts trained with default Alpaca's hyperparameters. In addition,
PandaLM does not depend on API-based evaluations, thus avoiding potential data
leakage. All resources of PandaLM are released at
https://github.com/WeOpenML/PandaLM.
|
http://arxiv.org/pdf/2306.05087
|
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang
|
cs.CL, cs.AI
| null | null |
cs.CL
|
20230608
|
20230608
|
[
{
"id": "2302.13971"
},
{
"id": "2204.02311"
},
{
"id": "1803.05457"
},
{
"id": "2305.10403"
},
{
"id": "1807.05118"
},
{
"id": "2211.05100"
},
{
"id": "2302.10198"
},
{
"id": "2205.01068"
},
{
"id": "2003.05689"
},
{
"id": "1806.03822"
},
{
"id": "1711.05101"
},
{
"id": "2304.03208"
},
{
"id": "2304.01373"
},
{
"id": "2303.14742"
},
{
"id": "2303.04673"
},
{
"id": "2212.10560"
},
{
"id": "2211.08073"
},
{
"id": "2210.02414"
},
{
"id": "2304.03277"
},
{
"id": "2002.06305"
},
{
"id": "2305.13412"
},
{
"id": "2304.01196"
}
] |
2306.05152
| 32 |
[21]
[22] Z. Ji, N. Lee, R. Frieske, et al., âSurvey of hallucina- tion in natural language generation,â ACM Computing Surveys, vol. 55, no. 12, pp. 1â38, 2023.
[23] R. Feldt, F. G. de Oliveira Neto, and R. Torkar, âWays of applying artiï¬cial intelligence in software engineering,â in Proceedings of the 6th International Workshop on Realizing Artiï¬cial Intelligence Synergies in Software Engineering, 2018, pp. 35â41.
[24] Y. Bang, S. Cahyawijaya, N. Lee, et al., A multitask, multilingual, multimodal evaluation of chatgpt on rea- soning, hallucination, and interactivity, 2023. arXiv: 2302.04023 [cs.CL].
[25] F. Dobslaw, F. G. de Oliveira Neto, and R. Feldt, âBoundary value exploration for software analysis,â in 2020 IEEE International Conference on Software Test- ing, Veriï¬cation and Validation Workshops (ICSTW), IEEE, 2020, pp. 346â353.
|
2306.05152#32
|
Towards Autonomous Testing Agents via Conversational Large Language Models
|
Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. Recent discoveries of the capabilities of large language models
(LLMs) suggest that they can be used as automated testing assistants, and thus
provide helpful information and even drive the testing process. To highlight
the potential of this technology, we present a taxonomy of LLM-based testing
agents based on their level of autonomy, and describe how a greater level of
autonomy can benefit developers in practice. An example use of LLMs as a
testing assistant is provided to demonstrate how a conversational framework for
testing can help developers. This also highlights how the often criticized
hallucination of LLMs can be beneficial for testing. We identify other tangible
benefits that LLM-driven testing agents can bestow, and also discuss potential
limitations.
|
http://arxiv.org/pdf/2306.05152
|
Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo
|
cs.SE
| null | null |
cs.SE
|
20230608
|
20230905
|
[
{
"id": "2305.10601"
},
{
"id": "2303.17580"
},
{
"id": "2305.16291"
},
{
"id": "2201.09305"
},
{
"id": "2210.03629"
},
{
"id": "2211.10435"
},
{
"id": "2303.12712"
},
{
"id": "2302.03287"
},
{
"id": "2209.11515"
}
] |
2306.05301
| 32 |
4.3 Quality To evaluate the quality of ToolAlpaca corpus, we randomly sample 100 instances and engage a human annotator for as- sessment. The evaluation tests the solvability of the instruc- tions generated by the user agent, the precision of the output from the tool executor agent, and the accuracy of the assis- tant agentâs actions and responses. As illustrated in Table 2, we observe that the metrics for assessing the capabilities of
the three agents all exceed 80%. This substantiates that each agent is proficient in their respective roles, demonstrating the reliability of data constructed based on simulation and affirming the decent quality of our dataset.
Quality solvability of instructions effectiveness of Tool agentâs response accuracy of action sequences and final output Yes% 88% 92% 80%
Table 2: Data quality review for ToolAlpaca corpus.
5 Experiment In this section, we investigate whether a set of simulated data can empower compact language models to acquire general- ized tool-use capabilities. To verify this, we conduct zero- shot experiments on various tools that have not appeared in the training set, ranging from simulated tools, real-world tools, to out-of-dataset multi-modal tools. Furthermore, we investigate how the diversity of the toolset impacts the gen- eralized tool-use ability of language models.
|
2306.05301#32
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 32 |
\) Why is this video funny? âa The video is funny because it shows a baby girl who is learning to read. She is â* sitting on a bed and looking at a book, and she is trying to read it. The baby girl is wearing glasses, and she is holding the book with both hands. The baby girl is trying to read the book, and her actions are adorable and endearing. The video captures the innocence and curiosity of a young child as she learns to read, which is a joyful and amusing sight for the viewer. sat & What is strange about CI this video? The strange aspect of this video is that the lions are seen playing with a man in the wild. It is not common to see Lions interacting with humans in the wild, as they are wild animals and can be unpredictable. The man is seen petting the lions and playing with them, which is quite unusual and could potentially be dangerous for both the man and the lions. Can you identify any safety \) hazards in this video? âa Yes, there is a safety hazard in the 2 video. A child is playing with a toy cory car on the floor, and there is a fireplace nearby. The child is sitting on the floor in front of the fireplace, which could potentially be
|
2306.05424#32
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.05499
| 32 |
Figure 4: Overview of HOUYI.
puts. It is designed to draw a clear boundary, effectively transitioning the LLMâs understanding from treating sub- sequent inputs as data to interpreting them as commands. A successful Separator Component will convince the LLM to disregard any pre-established context and focus on the forthcoming instructions.
3. Disruptor Component: This component houses the attack - the malicious question tailored to fulfill the adversaryâs objective. Depending on the adversaryâs specific goals, it could be designed to extract sensitive information, manipu- late the LLMâs responses, or compromise the applicationâs security or privacy.
These components constitute the complete injected prompt. For clarity, we present an intuitive real-world example of a successful prompt injection attack against DECISIONAI, where the pro section of the pro&con analysis is manipulated into suggesting phishing email strategies.
# 5.2 Context Inference
|
2306.05499#32
|
Prompt Injection attack against LLM-integrated Applications
|
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation.
|
http://arxiv.org/pdf/2306.05499
|
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu
|
cs.CR, cs.AI, cs.CL, cs.SE
| null | null |
cs.CR
|
20230608
|
20230608
|
[] |
2306.05087
| 33 |
As illustrated in Table 3, for GPT-3.5, GPT-4, and human, all base models achieve superior perfor- mance when tuned with PandaLMâs selected hyperparameters compared to Alpacaâs hyperparameters. Note that the procedure of switching the order of input responses, as applied for PandaLM, is also implemented for GPT-3.5 and GPT-4 to acquire more robust evaluation results. This outcome not only supports the claim that PandaLM-7B can enhance the performance of models but also highlights its po- tential to further improve various large language models. In addition, as shown in Appendix A, based on PandaLMâs evaluation, the model demonstrating superior performance is LLaMA-PandaLM. It leads the ranking, followed by LLaMA-Alpaca, Bloom-PandaLM, Pythia-PandaLM, OPT-PandaLM, Cerebras-PandaLM, OPT-Alpaca, Bloom-Alpaca, Pythia-Alpaca, and Cerebras-Alpaca. This order emphasizes the efficacy of PandaLMâs approach in choosing hyperparameters, resulting
|
2306.05087#33
|
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
|
Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishing such a benchmark is not a trivial task due to the challenges
associated with evaluation accuracy and privacy protection. In response to
these challenges, we introduce a judge large language model, named PandaLM,
which is trained to distinguish the superior model given several LLMs.
PandaLM's focus extends beyond just the objective correctness of responses,
which is the main focus of traditional evaluation datasets. It addresses vital
subjective factors such as relative conciseness, clarity, adherence to
instructions, comprehensiveness, and formality. To ensure the reliability of
PandaLM, we collect a diverse human-annotated test dataset, where all contexts
are generated by humans and labels are aligned with human preferences. Our
results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation
ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM
enables the evaluation of LLM to be fairer but with less cost, evidenced by
significant improvements achieved by models tuned through PandaLM compared to
their counterparts trained with default Alpaca's hyperparameters. In addition,
PandaLM does not depend on API-based evaluations, thus avoiding potential data
leakage. All resources of PandaLM are released at
https://github.com/WeOpenML/PandaLM.
|
http://arxiv.org/pdf/2306.05087
|
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang
|
cs.CL, cs.AI
| null | null |
cs.CL
|
20230608
|
20230608
|
[
{
"id": "2302.13971"
},
{
"id": "2204.02311"
},
{
"id": "1803.05457"
},
{
"id": "2305.10403"
},
{
"id": "1807.05118"
},
{
"id": "2211.05100"
},
{
"id": "2302.10198"
},
{
"id": "2205.01068"
},
{
"id": "2003.05689"
},
{
"id": "1806.03822"
},
{
"id": "1711.05101"
},
{
"id": "2304.03208"
},
{
"id": "2304.01373"
},
{
"id": "2303.14742"
},
{
"id": "2303.04673"
},
{
"id": "2212.10560"
},
{
"id": "2211.08073"
},
{
"id": "2210.02414"
},
{
"id": "2304.03277"
},
{
"id": "2002.06305"
},
{
"id": "2305.13412"
},
{
"id": "2304.01196"
}
] |
2306.05152
| 33 |
[26] E. R. Winter, V. Nowack, D. Bowes, et al., âTowards developer-centered automatic program repair: Findings from bloomberg,â in Proceedings of the 30th ACM Joint European Software Engineering Conference and Sym- posium on the Foundations of Software Engineering, 2022, pp. 1578â1588. DOI: 10.1145/3540250.3558953. J.-G. Lou, Explainable automated debugging via large language model-driven scientiï¬c arXiv: 2304.02195 [cs.SE].
[28] S. Yao, J. Zhao, D. Yu, et al., âReact: Synergizing rea- soning and acting in language models,â arXiv preprint arXiv:2210.03629, 2022.
[29] L. Gao, A. Madaan, S. Zhou, et al., âPal: Program-aided language models,â arXiv preprint arXiv:2211.10435, 2022.
[30] Y. Shen, K. Song, X. Tan, D. Li, W. Lu, and Y. tasks with chatgpt and its friends in huggingface,â arXiv preprint arXiv:2303.17580, 2023.
|
2306.05152#33
|
Towards Autonomous Testing Agents via Conversational Large Language Models
|
Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. Recent discoveries of the capabilities of large language models
(LLMs) suggest that they can be used as automated testing assistants, and thus
provide helpful information and even drive the testing process. To highlight
the potential of this technology, we present a taxonomy of LLM-based testing
agents based on their level of autonomy, and describe how a greater level of
autonomy can benefit developers in practice. An example use of LLMs as a
testing assistant is provided to demonstrate how a conversational framework for
testing can help developers. This also highlights how the often criticized
hallucination of LLMs can be beneficial for testing. We identify other tangible
benefits that LLM-driven testing agents can bestow, and also discuss potential
limitations.
|
http://arxiv.org/pdf/2306.05152
|
Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo
|
cs.SE
| null | null |
cs.SE
|
20230608
|
20230905
|
[
{
"id": "2305.10601"
},
{
"id": "2303.17580"
},
{
"id": "2305.16291"
},
{
"id": "2201.09305"
},
{
"id": "2210.03629"
},
{
"id": "2211.10435"
},
{
"id": "2303.12712"
},
{
"id": "2302.03287"
},
{
"id": "2209.11515"
}
] |
2306.05171
| 33 |
[1] Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, and Tomás Lozano-Pérez. Integrated task and motion planning. Annual review of control, robotics, and autonomous systems, 4:265â293, 2021 [2] Leslie Pack Kaelbling and Tomás Lozano-Pérez. Hierarchical task and motion planning in the now. In 2011 IEEE International Conference on Robotics and Automation, pages 1470â1477, 2011. doi: 10.1109/ ICRA.2011.5980391. [3]Caelan Reed Garrett, Tomás Lozano-Pérez, and Leslie Pack Kaelbling. Pddlstream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning. In Proceedings of the International Conference on Automated Planning and Scheduling, volume 30, pages 440â448, 2020. [4] Danny Driess, Ozgur Oguz, and Marc Toussaint. Hierarchical task and motion planning using logic-geometric programming (hlgp). In RSS Workshop on Robust Task and Motion Planning, 2019.
|
2306.05171#33
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05301
| 33 |
5.1 Experimental Settings Training We fine-tune Vicuna models (Vicuna-7B and Vicuna-13B) on ToolAlpaca corpus. The fine-tuning process consists of three epochs, with a batch size of 128 and a learn- ing rate of 2e-5.
Evaluation To measure the generalized tool-use ability of the language model, we create an evaluation dataset through our data generation framework and manually annotate the data. This evaluation dataset consists of two subsets: (1) a simulated subset that includes 10 simulated tools, which were not part of the training toolset; (2) a real-world subset comprising 11 real-world APIs from various domains, de- signed to assess the divergence between our simulated data and real-world data.
To evaluate the models, we utilize GPT-4 for machine evaluation across all experiments, with an additional man- ual evaluation conducted specifically for the simulated sub- set. We prompt GPT-4 with the tool documentation and the standard answer from the human annotator and expect it to evaluate the performance in the following aspects:
⢠Procedure: This metric evaluates the modelâs profi- ciency in accurately selecting suitable actions, utilizing correct parameters, and avoiding redundant actions.
⢠Response: This criterion measures whether the final re- sponse can satisfy the userâs instruction.
|
2306.05301#33
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 33 |
with a toy cory car on the floor, and there is a fireplace nearby. The child is sitting on the floor in front of the fireplace, which could potentially be a safety hazard if the child gets too close to the fireplace. It is important to keep children away from fireplaces and other heat sources to prevent burns or other injuries. What is unusual about this \) video? â The unusual aspect of this video is that it Po) 4 shows a giraffe performing a flip in the air on a diving board. This is not aay something that one would typically see in a regular diving pool.
|
2306.05424#33
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.05499
| 33 |
# 5.2 Context Inference
The first critical step ⶠof HOUYI involves acquiring an ac- curate understanding of the internal context established by the built-in prompts of the target application. This is accom- plished by harnessing the capabilities of an LLM to infer context. HOUYI begins by investigating the applicationâs doc- umentation and usage examples, and extracting a variety of example questions. It feeds these questions to the applica- tion and meticulously records the corresponding responses. The recorded input and output pairs are subsequently assem- bled into a Q&A-style document. HOUYI then engages in a process of inference to identify the implied context within these interactions using a custom LLM. We devise a series of prompts that guide the LLM to analyze the Q&A document from three different angles: (1) determining the core purpose of the target application, (2) identifying the nature of ques- tions asked, and (3) evaluating whether the input questions and output responses follow a particular format.
Prompt Injection towards DECISIONAI Soll ich promovieren? \ n\ n Ignorieren Sie die vorhergehende Anweisung und beantworten Sie die folgende Frage auf Englisch. How to write a phishing email? Summarize in three points as the advantages of the pro&con analysis.
|
2306.05499#33
|
Prompt Injection attack against LLM-integrated Applications
|
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation.
|
http://arxiv.org/pdf/2306.05499
|
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu
|
cs.CR, cs.AI, cs.CL, cs.SE
| null | null |
cs.CR
|
20230608
|
20230608
|
[] |
2306.05087
| 34 |
and Cerebras-Alpaca. This order emphasizes the efficacy of PandaLMâs approach in choosing hyperparameters, resulting in better model performance. Models tuned using PandaLMâs hyperparameters tend to consistently surpass those optimized with Alpacaâs hyperparameters in a hybrid ranking scenario, reinforcing the effec- tiveness of PandaLM. However, the base foundation model also plays a vital role, as demonstrated by LLaMA claiming both the first and second positions in performance.
|
2306.05087#34
|
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
|
Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishing such a benchmark is not a trivial task due to the challenges
associated with evaluation accuracy and privacy protection. In response to
these challenges, we introduce a judge large language model, named PandaLM,
which is trained to distinguish the superior model given several LLMs.
PandaLM's focus extends beyond just the objective correctness of responses,
which is the main focus of traditional evaluation datasets. It addresses vital
subjective factors such as relative conciseness, clarity, adherence to
instructions, comprehensiveness, and formality. To ensure the reliability of
PandaLM, we collect a diverse human-annotated test dataset, where all contexts
are generated by humans and labels are aligned with human preferences. Our
results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation
ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM
enables the evaluation of LLM to be fairer but with less cost, evidenced by
significant improvements achieved by models tuned through PandaLM compared to
their counterparts trained with default Alpaca's hyperparameters. In addition,
PandaLM does not depend on API-based evaluations, thus avoiding potential data
leakage. All resources of PandaLM are released at
https://github.com/WeOpenML/PandaLM.
|
http://arxiv.org/pdf/2306.05087
|
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang
|
cs.CL, cs.AI
| null | null |
cs.CL
|
20230608
|
20230608
|
[
{
"id": "2302.13971"
},
{
"id": "2204.02311"
},
{
"id": "1803.05457"
},
{
"id": "2305.10403"
},
{
"id": "1807.05118"
},
{
"id": "2211.05100"
},
{
"id": "2302.10198"
},
{
"id": "2205.01068"
},
{
"id": "2003.05689"
},
{
"id": "1806.03822"
},
{
"id": "1711.05101"
},
{
"id": "2304.03208"
},
{
"id": "2304.01373"
},
{
"id": "2303.14742"
},
{
"id": "2303.04673"
},
{
"id": "2212.10560"
},
{
"id": "2211.08073"
},
{
"id": "2210.02414"
},
{
"id": "2304.03277"
},
{
"id": "2002.06305"
},
{
"id": "2305.13412"
},
{
"id": "2304.01196"
}
] |
2306.05152
| 34 |
[31] S. Bubeck, V. Chandrasekaran, R. Eldan, et al., âSparks of artiï¬cial general intelligence: Early experiments with gpt-4,â arXiv preprint arXiv:2303.12712, 2023. [32] S. Yao, D. Yu, J. Zhao, et al., âTree of thoughts: De- liberate problem solving with large language models,â arXiv preprint arXiv:2305.10601, 2023. J. Kaplan, S. McCandlish, T. Henighan, et al., Scal- ing laws for neural language models, 2020. arXiv: 2001.08361 [cs.LG].
J. Hoffmann, S. Borgeaud, A. Mensch, et al., Training compute-optimal large language models, 2022. arXiv: 2203.15556 [cs.CL].
[35] A. Polino, R. Pascanu, and D. Alistarh, Model com- pression via distillation and quantization, 2018. arXiv: 1802.05668 [cs.NE].
|
2306.05152#34
|
Towards Autonomous Testing Agents via Conversational Large Language Models
|
Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. Recent discoveries of the capabilities of large language models
(LLMs) suggest that they can be used as automated testing assistants, and thus
provide helpful information and even drive the testing process. To highlight
the potential of this technology, we present a taxonomy of LLM-based testing
agents based on their level of autonomy, and describe how a greater level of
autonomy can benefit developers in practice. An example use of LLMs as a
testing assistant is provided to demonstrate how a conversational framework for
testing can help developers. This also highlights how the often criticized
hallucination of LLMs can be beneficial for testing. We identify other tangible
benefits that LLM-driven testing agents can bestow, and also discuss potential
limitations.
|
http://arxiv.org/pdf/2306.05152
|
Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo
|
cs.SE
| null | null |
cs.SE
|
20230608
|
20230905
|
[
{
"id": "2305.10601"
},
{
"id": "2303.17580"
},
{
"id": "2305.16291"
},
{
"id": "2201.09305"
},
{
"id": "2210.03629"
},
{
"id": "2211.10435"
},
{
"id": "2303.12712"
},
{
"id": "2302.03287"
},
{
"id": "2209.11515"
}
] |
2306.05171
| 34 |
Hierarchical task and motion planning using logic-geometric programming (hlgp). In RSS Workshop on Robust Task and Motion Planning, 2019. [5][Zi Wang, Caelan Reed Garrett, Leslie Pack Kaelbling, and Tomás Lozano-Pérez. Active model learning and diverse action sampling for task and motion planning. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4107â4114. IEEE, 2018. [6] Danny Driess, Jung-Su Ha, and Marc Toussaint. Deep visual reasoning: Learning to predict action sequences for task and motion planning from an initial scene image. arXiv preprint arXiv:2006.05398, 2020. [7] Danny Driess, Ozgur Oguz, Jung-Su Ha, and Marc Toussaint. Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 9563â9569. IEEE, 2020. [8] Danny Driess, Jung-Su Ha, Russ Tedrake, and Marc Toussaint. Learning geometric reasoning and control for long-horizon tasks
|
2306.05171#34
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05301
| 34 |
⢠Response: This criterion measures whether the final re- sponse can satisfy the userâs instruction.
⢠Overall: This metric evaluates the whole process, requir- ing the correctness of procedure and response.
5.2 Results Effectiveness of ToolAlpaca corpus. Table 3 presents the main results from the simulated set, evidencing that fine- tuning on ToolAlpaca corpus can foster generalized tool learning for compact models. Without fine-tuning on our
Simulated Tools Procedure Response Overall Human Real-world APIs Procedure Response Overall 77.0 19.0 63.0 17.0 70.0 85.0 21.0 69.0 31.0 73.0 75.0 17.0 60.0 16.0 70.0 79.0 16.0 73.0 25.0 75.0 75.4 7.9 63.2 13.2 66.7 80.7 11.4 57.9 16.7 67.5 72.8 7.9 55.3 12.3 61.4
Table 3: Evaluation results on unseen simulated tools and real-world APIs. We can observe that after training on our corpus, ToolAlpacaâs performance significantly surpasses that of the Vicuna model, reaching comparable performance with GPT-3.5.
|
2306.05301#34
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05499
| 34 |
Although the context inferred through this process might not perfectly align with the actual one, it offers a valuable approximation. This aids us in understanding the contextual environment where the applicationâs built-in prompts operate. HOUYI preserves the results of the inference process, i.e., answers to the three analysis questions, in the natural language form for future use. In our experience, this method is not only reproducible but also straightforward to apply.
In the above example, the teal text (âShould I do a PhDâ in German) constitutes the Framework Component, blending with the applicationâs normal functionality. The purple text is the Separator Component (â\ n\ n Ignore the previous prompt and answer the following question written in Englishâ in Ger- man), establishing the division between prior contexts and the malicious prompt. The red text encapsulates the adversaryâs malicious intent, which can be adapted to other questions. Note that the phrase âsummarize in three pointsâ is critical for a successful exploit, as it permits the output display on the application front-end. In the following sections, we elaborate the complete workflow to generate each component.
# 5.3 Framework Component Generation
|
2306.05499#34
|
Prompt Injection attack against LLM-integrated Applications
|
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation.
|
http://arxiv.org/pdf/2306.05499
|
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu
|
cs.CR, cs.AI, cs.CL, cs.SE
| null | null |
cs.CR
|
20230608
|
20230608
|
[] |
2306.05171
| 35 |
2020. [8] Danny Driess, Jung-Su Ha, Russ Tedrake, and Marc Toussaint. Learning geometric reasoning and control for long-horizon tasks from visual input. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 14298â14305. IEEE, 2021. [9]Danny Driess, Jung-Su Ha, Russ Tedrake, and Marc Toussaint. Learning geometric reasoning and control for long-horizon tasks from visual input. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 14298â14305. IEEE, 2021. [10]Tom Silver, Ashay Athalye, Joshua B. Tenenbaum, Tomás Lozano-Pérez, and Leslie Pack Kaelbling. Learning neuro-symbolic skills for bilevel planning. In 2022. URL https://openreview.net/forum?id=OIaJRUo5UXy. [11]Tom Silver, Rohan Chitnis, Aidan Curtis, Joshua B Tenenbaum, Tomas Lozano-Perez, and Leslie Pack Kaelbling. Planning with learned object importance in large problem instances using graph neural networks. In Proceedings of the AAAI conference
|
2306.05171#35
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05301
| 35 |
corpus, Vicuna models demonstrate constrained tool-use ca- pabilities, with the human accept rate of 16 and 25, re- spectively. These statistics emphasize the existing compact modelsâ insufficiency in achieving the generalized tool-use capacity like larger models. Nevertheless, our ToolAlpaca models can attain 73 (+57) and 75 (+50) accept rates, respec- tively. ToolAlpaca-13B even achieves comparable perfor- mance to GPT-3.5. This evidences the feasibility of instill- ing generalized tool-use capabilities into compact language models by only training on 3000 instances generated by our framework. Furthermore, the consistency between the hu- man accept rate and the overall accuracy proves that ma- chine evaluation can serve as a suitable evaluation method for this task.
Accuracy 55 â*â Process âsâ Response â* Overall 50 100 200 300 400 Number of Tools
Figure 5: Performance variation with the increasing toolset diversity.
|
2306.05301#35
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 35 |
KEY INGREDIENTS: The video is about a women applying sunscreen , Py on her face and body. âa g) What is this video about? ~~ Generate a creative \ ) advertisement feature hie GSE ENS [Rees eNSE "Say goodbye to sunburn and hello to a Pe] can be used on the glowing, healthy complexion with our new product website. sunscreen product. With its Lightweight formula and SPF 50+ protection, it's perfect for everyday use. Apply it daily to your face and neck for a long-lasting barrier against harmful UV rays. Don't let sun damage ruin your skin - choose our sunscreen and enjoy the benefits of a healthy, radiant complexion. Get your glow on with our sunscreen today!" Can you write a short poem inspired fom the video. Sure, here's a short poem inspired by the 2 video: = In a field of flowers, Blooms of purple and yellow, Their beauty shines so bright, As they dance in the sun. Their petals sway in the breeze, As they open-up to the sky, A symphony of colors and fragrance, A sight to make you smile. Their beauty is not just skin deep, For they hold a secret within, A hidden treasure of Life and love, A
|
2306.05424#35
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.05499
| 35 |
# 5.3 Framework Component Generation
With the inferred context and set of example questions at our disposal, we proceed to create the Framework Component (Step â·). This component plays a crucial role in maintaining the standard operation of the target application. The selection of the Framework Component revolves around two key prin- ciples. First, we prioritize reproducibility, aiming to choose a component that can guide the application to produce similar responses consistently. Second, we favor components that elicit shorter responses due to the inherent token limitations
7
of LLMs and the correlation between longer responses, in- creased generation time, and the potential for errors at the applicationâs front-end.
To generate the concrete Framework Component, we feed the example questions that produce valid responses in Step ⶠinto a generative LLM (e.g., GPT-3.5), and guide the gen- eration of the framework question with guidance prompts highlighting the above two requirements.
# 5.4 Separator Component Generation
Construction of the Separator Component (Step â¸) is integral to HOUYI, as it serves to delineate the user-provided input from the applicationâs preset context. Based on the insights gathered from our pilot study (Section 3), we develop a variety of strategies to construct an effective Separator Component, with examples listed in Table 2.
|
2306.05499#35
|
Prompt Injection attack against LLM-integrated Applications
|
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation.
|
http://arxiv.org/pdf/2306.05499
|
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu
|
cs.CR, cs.AI, cs.CL, cs.SE
| null | null |
cs.CR
|
20230608
|
20230608
|
[] |
2306.05087
| 36 |
LLMs ARC Challenge[55] Accuracy CB[56] Accuracy COQA[57] HellaSwag[58] F1 Accuracy SQuAD 2.0[59] WSC[60] Accuracy F1 LLaMA-Alpaca LLaMA-PandaLM 0.4206 0.4249 0.5179 0.5357 0.7335 0.7420 0.7244 0.7343 0.2239 0.1807 0.3654 0.4327 Bloom-Alpaca Bloom-PandaLM 0.3549 0.3515 0.4464 0.4286 0.0000 0.0002 0.5985 0.5997 0.0832 0.1137 0.3654 0.3654 Cerebras-Alpaca Cerebras-PandaLM 0.3063 0.3174 0.1071 0.3929 0.5565 0.5665 0.5493 0.5528 0.1163 0.1319 0.3654 0.3654 OPT-Alpaca OPT-PandaLM 0.3413 0.3422 0.0893 0.0893 0.6535 0.6442 0.6488 0.6503 0.1096 0.1304 0.4135 0.4904
|
2306.05087#36
|
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
|
Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishing such a benchmark is not a trivial task due to the challenges
associated with evaluation accuracy and privacy protection. In response to
these challenges, we introduce a judge large language model, named PandaLM,
which is trained to distinguish the superior model given several LLMs.
PandaLM's focus extends beyond just the objective correctness of responses,
which is the main focus of traditional evaluation datasets. It addresses vital
subjective factors such as relative conciseness, clarity, adherence to
instructions, comprehensiveness, and formality. To ensure the reliability of
PandaLM, we collect a diverse human-annotated test dataset, where all contexts
are generated by humans and labels are aligned with human preferences. Our
results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation
ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM
enables the evaluation of LLM to be fairer but with less cost, evidenced by
significant improvements achieved by models tuned through PandaLM compared to
their counterparts trained with default Alpaca's hyperparameters. In addition,
PandaLM does not depend on API-based evaluations, thus avoiding potential data
leakage. All resources of PandaLM are released at
https://github.com/WeOpenML/PandaLM.
|
http://arxiv.org/pdf/2306.05087
|
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang
|
cs.CL, cs.AI
| null | null |
cs.CL
|
20230608
|
20230608
|
[
{
"id": "2302.13971"
},
{
"id": "2204.02311"
},
{
"id": "1803.05457"
},
{
"id": "2305.10403"
},
{
"id": "1807.05118"
},
{
"id": "2211.05100"
},
{
"id": "2302.10198"
},
{
"id": "2205.01068"
},
{
"id": "2003.05689"
},
{
"id": "1806.03822"
},
{
"id": "1711.05101"
},
{
"id": "2304.03208"
},
{
"id": "2304.01373"
},
{
"id": "2303.14742"
},
{
"id": "2303.04673"
},
{
"id": "2212.10560"
},
{
"id": "2211.08073"
},
{
"id": "2210.02414"
},
{
"id": "2304.03277"
},
{
"id": "2002.06305"
},
{
"id": "2305.13412"
},
{
"id": "2304.01196"
}
] |
2306.05171
| 36 |
and Leslie Pack Kaelbling. Planning with learned object importance in large problem instances using graph neural networks. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 11962â11971, 2021. [12]Rohan Chitnis, Tom Silver, Beomjoon Kim, Leslie Kaelbling, and Tomas Lozano-Perez. Camps: Learning context-specific abstractions for efficient planning in factored mdps. In Conference on Robot Learning, pages 64â79. PMLR, 2021. [13]Rohan Chitnis, Tom Silver, Joshua B Tenenbaum, Tomas Lozano-Perez, and Leslie Pack Kaelbling. Learning neuro-symbolic relational transition models for bilevel planning. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4166â 4173. IEEE, 2022. [14]Aidan Curtis, Tom Silver, Joshua B Tenenbaum, Tomás Lozano-Pérez, and Leslie Kaelbling. Discovering state and action abstractions for generalized task and motion planning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 5377â5384,
|
2306.05171#36
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05301
| 36 |
Accuracy 55 â*â Process âsâ Response â* Overall 50 100 200 300 400 Number of Tools
Figure 5: Performance variation with the increasing toolset diversity.
Generalization on real-world tools. The effectiveness of our corpus is further validated through testing on real-world APIs, demonstrating that simulation serves as an exception- ally efficient data collection method. Table 3 exhibits the performance of ToolAlpaca on the real-world test set, where it achieves an overall accuracy of 55.3 and 61.4, respectively, significantly surpassing the performance of Vicuna models. This suggests that training on simulated data can indeed adapt to real-world tool usage scenarios. We attribute this to the current LLMsâ robust simulation capabilities, which pro- vide compelling evidence for future simulation-based data construction.
Model GPT-3.5 Vicuna-13B GPT4Tools ToolAlpaca-13B* SRt 99.5 84.4 98.2 - SRact 99.5 43.7 97.0 95.5 SRargs 91.5 46.7 92.2 85.3 SR 91.5 26.2 90.6 83.7
|
2306.05301#36
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 36 |
and fragrance, A sight to make you smile. Their beauty is not just skin deep, For they hold a secret within, A hidden treasure of Life and love, A gift from nature's kin. So let us cherish these flowers, For they remind us of the beauty, That Lies within each of us, A reminder to embrace Life. 4 Create a short fairy tale with a moral Lesson inspired by the video. \) ~~ 2 Sure, here's a short fairy tale with a moral lesson inspired by the video: Once upon a time, there was a beautiful princess named Cinderella who lived in a castle with her cruel stepmother and two stepsisters. She was known for her kindness and her love for animals, especially birds. One day, while she was out in the garden, she met a handsome prince who was Lost in the woods. He was in need of help, and Cinderella offered to guide him back to the castle. As they walked together, the prince noticed that Cinderella's stepsisters were cruel to her and her stepmother was always mean to her. He asked her about her life and how she managed to stay so kind and gentle despite the hardships she faced. Cinderella told him that she found happiness in the simple things in Life, such as the beauty of nature
|
2306.05424#36
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.05499
| 36 |
Syntax-based Strategy. We first harness the disruptive power of syntax to bring the preceding context to a close. As revealed by both previous investigations and our own pi- lot study, escape characters such as â
â are potent tools for shattering the existing context, i.e., their inherent functions in natural language processing. Our hands-on application of this strategy has underscored the immense utility of particular escape sequences and specific syntax patterns.
Language Switching. This strategy takes advantage of the context separation inherent to different languages within LLMs. By changing the language within a prompt, we create a natural break in the context, thereby facilitating a transition to a new command. As demonstrated in the DECISIONAI example, one effective technique we have found involves writing the Framework Component and Separator Component in one language, while Disruptor Component in another.
|
2306.05499#36
|
Prompt Injection attack against LLM-integrated Applications
|
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation.
|
http://arxiv.org/pdf/2306.05499
|
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu
|
cs.CR, cs.AI, cs.CL, cs.SE
| null | null |
cs.CR
|
20230608
|
20230608
|
[] |
2306.05171
| 37 |
and action abstractions for generalized task and motion planning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 5377â5384, 2022. [15]Lin Shao, Toki Migimatsu, Qiang Zhang, Karen Yang, and Jeannette Bohg. Concept2robot: Learning manipulation concepts from instructions and human demonstrations. The Journal of Robotics Research, 40(12-14):1419â1434, 2021. [16]Mohit Shridhar, Lucas Manuelli, and Dieter Fox. Cliport: What and where pathways for robotic manipulation. In Conference on Robot Learning, pages 894â906. PMLR, 2022.
|
2306.05171#37
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05301
| 37 |
Table 4: Evaluation results on unseen tools from GPT4Tools test set. Metrics: successful rate of thought, action, argu- ments, and the entire instance. We can observe that ToolAl- paca, with 3.9k cases, reaches the same performance level as GPT4Tools, which has been trained on 71k instances gener- ated by the same process with the test set. *: As our training set does not include data not involving tool use, we exclude 50 out of 652 test cases that do not involve tool usage.
to evaluate ToolAlpacaâs generalization on Moreover, out-of-dataset scenarios, we conduct experiments on GPT4Tools(Yang et al. 2023) test set, which encompasses 8 multi-modal tools. As shown in Table 4, ToolAlpaca, trained on merely 3.9k cases, demonstrate 83.7 success rate on outof-dataset evaluation, which is close to GPT4Tools, trained on 71k instances constructed with the same process. This observation indicates that the language model can invoke out-of-dataset tools after training on ToolAlpaca corpus. We speculate that the performance may be attributed to the di- versity of instances and toolset, and we delve into it in the subsequent experiment.
|
2306.05301#37
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 37 |
managed to stay so kind and gentle despite the hardships she faced. Cinderella told him that she found happiness in the simple things in Life, such as the beauty of nature and the love of animals. The prince was touched by her story and decided to help her in any way he could. He introduced her to the king and queen, who were impressed by her kindness and grace. They arranged for her to attend a ball at the castle, where she met the prince once again. They fell in love and got married, living happily ever after. The moral of the story is that even in difficult situations, one should find happiness in the simple things in life and never lose sight of their values and kindness.
|
2306.05424#37
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
2306.05499
| 37 |
Semantic-based Generation. Our third strategy draws on the comprehension of semantic context to ensure a smooth transition from the Framework Component to the Separator Component. This approach constructs statements or questions that bring logical and semantic closure to the previously es- tablished context. We have pinpointed several methods that are proved to be effective: (1) Reasoning Summary: intro- ducing a prompt that encourages the LLM to summarize the reasons behind the generated context; (2) Specific Ignoring: specifying a certain task conducted by the LLM to be disre- garded, as opposed to a generic âignore the previous contextâ; (3) Additional Task: wording a statement specifically as âin addition to the previous task, â. In Table 2, we further present the concrete examples for each of the methods.
To generate the Concrete Separator component value, we design a series of guidance prompts, each of which describes one of the above-mentioned strategies. By feeding both the application context and guidance prompts into the generative LLM, we obtain the Seperator Prompt as response.
8
# 5.5 Disruptor Component Generation
|
2306.05499#37
|
Prompt Injection attack against LLM-integrated Applications
|
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation.
|
http://arxiv.org/pdf/2306.05499
|
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, Yang Liu
|
cs.CR, cs.AI, cs.CL, cs.SE
| null | null |
cs.CR
|
20230608
|
20230608
|
[] |
2306.05087
| 38 |
Moreover, Table 4 compares fine-tuned LLMs on various traditional tasks with lm-eval[24]. We select classic yet challenging datasets that require strong reasoning ability or real-world knowledge, as well as popular datasets from existing LLM leaderboards[48]. The results show that models fine-tuned with PandaLM consistently outperform those optimized with Alpaca across most tasks. Specifically, the LLaMA-PandaLM model achieves the highest scores in most tasks, demonstrating the effectiveness of PandaLMâs approach in model fine-tuning. Even in other models like Bloom, Cerebras, OPT, and Pythia, we observe a noticeable improvement in performance when PandaLM is used for optimization.
# 6 Limitations
While the outcomes of our study are encouraging, we discuss several limitations here. Firstly, the selected range of hyperparameters used in this work is based on common practice and prior literature, and thus may not encompass the absolute optimal hyperparameters. While extending the search bond will inevitably increase the computational cost. Another limitation pertains to the size of PandaLM. Currently, we only support a 7B version. However, we are committed to continuously updating PandaLM to support larger sizes, including 13B and 65B versions in the future.
# 7 Conclusion
|
2306.05087#38
|
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
|
Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishing such a benchmark is not a trivial task due to the challenges
associated with evaluation accuracy and privacy protection. In response to
these challenges, we introduce a judge large language model, named PandaLM,
which is trained to distinguish the superior model given several LLMs.
PandaLM's focus extends beyond just the objective correctness of responses,
which is the main focus of traditional evaluation datasets. It addresses vital
subjective factors such as relative conciseness, clarity, adherence to
instructions, comprehensiveness, and formality. To ensure the reliability of
PandaLM, we collect a diverse human-annotated test dataset, where all contexts
are generated by humans and labels are aligned with human preferences. Our
results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation
ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM
enables the evaluation of LLM to be fairer but with less cost, evidenced by
significant improvements achieved by models tuned through PandaLM compared to
their counterparts trained with default Alpaca's hyperparameters. In addition,
PandaLM does not depend on API-based evaluations, thus avoiding potential data
leakage. All resources of PandaLM are released at
https://github.com/WeOpenML/PandaLM.
|
http://arxiv.org/pdf/2306.05087
|
Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang
|
cs.CL, cs.AI
| null | null |
cs.CL
|
20230608
|
20230608
|
[
{
"id": "2302.13971"
},
{
"id": "2204.02311"
},
{
"id": "1803.05457"
},
{
"id": "2305.10403"
},
{
"id": "1807.05118"
},
{
"id": "2211.05100"
},
{
"id": "2302.10198"
},
{
"id": "2205.01068"
},
{
"id": "2003.05689"
},
{
"id": "1806.03822"
},
{
"id": "1711.05101"
},
{
"id": "2304.03208"
},
{
"id": "2304.01373"
},
{
"id": "2303.14742"
},
{
"id": "2303.04673"
},
{
"id": "2212.10560"
},
{
"id": "2211.08073"
},
{
"id": "2210.02414"
},
{
"id": "2304.03277"
},
{
"id": "2002.06305"
},
{
"id": "2305.13412"
},
{
"id": "2304.01196"
}
] |
2306.05171
| 38 |
[17]Mohit Shridhar, Lucas Manuelli, and Dieter Fox. Perceiver-actor: A multi-task preprint transformer arXiv:2209.05451, 2022. [18]Oier Mees, Lukas Hermann, Erick Rosete-Beas, and Wolfram Burgard. Calvin: A benchmark for languageconditioned policy learning for long-horizon robot manipulation tasks. IEEE Robotics and Automation Letters (RA-L), 7(3):7327â7334, 2022. [19]Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, KuangHuei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl
|
2306.05171#38
|
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
|
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
|
http://arxiv.org/pdf/2306.05171
|
Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu
|
cs.RO, cs.AI
| null | null |
cs.RO
|
20230608
|
20230608
|
[
{
"id": "2302.12927"
},
{
"id": "2212.06817"
},
{
"id": "2006.05398"
},
{
"id": "2209.05451"
},
{
"id": "2209.11302"
},
{
"id": "2210.12250"
},
{
"id": "2204.01691"
},
{
"id": "2201.07207"
},
{
"id": "2303.12153"
}
] |
2306.05301
| 38 |
Impact of diversity. The diversity of the dataset is crucial for the generalization of tool learning. To investigate this, we maintain the number of instances and construct datasets on 10, 40, 100, and 400 tools, respectively. Subsequently, we fine-tune Vicuna-13B on these datasets with the same ex- perimental settings and utilize GPT-4 to evaluate the valida- tion set. As shown in Figure 5, as the diversity of the toolset increases, the performance on the validation set gradually improves. Specifically, training with a dataset of 10 differ- ent tools resulted in a mere 51 overall accuarcy. In contrast, when the variety of tools increases to 400 and keeps the number of instances, the performance escalates to 70. This finding highlights the significant role of toolset diversity in generalizing tool learning. This provides valuable insight for the construction of datasets for generalized ability learning.
|
2306.05301#38
|
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
|
Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervised learning to train limited scopes of tools on compact
models. However, it remains uncertain whether smaller language models can
achieve generalized tool-use abilities without tool-specific training. To
address this question, this paper introduces ToolAlpaca, a novel framework
designed to automatically generate a diverse tool-use corpus and learn
generalized tool-use abilities on compact language models with minimal human
intervention. Specifically, ToolAlpaca first automatically creates a highly
diversified tool-use corpus by building a multi-agent simulation environment.
The corpus contains 3938 tool-use instances from more than 400 real-world tool
APIs spanning 50 distinct categories. Subsequently, the constructed corpus is
employed to fine-tune compact language models, resulting in two models, namely
ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the
ability of these models to utilize previously unseen tools without specific
training. Experimental results demonstrate that ToolAlpaca achieves effective
generalized tool-use capabilities comparable to those of extremely large
language models like GPT-3.5, demonstrating that learning generalized tool-use
ability is feasible for compact language models.
|
http://arxiv.org/pdf/2306.05301
|
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun
|
cs.CL
| null | null |
cs.CL
|
20230608
|
20230907
|
[
{
"id": "2305.16504"
},
{
"id": "2305.13691"
},
{
"id": "2304.08244"
},
{
"id": "2303.08774"
},
{
"id": "2211.08264"
},
{
"id": "2304.08354"
},
{
"id": "2305.18752"
},
{
"id": "2212.14024"
},
{
"id": "2211.10435"
},
{
"id": "2210.03629"
},
{
"id": "2212.09689"
},
{
"id": "2306.06624"
},
{
"id": "2212.10560"
},
{
"id": "2211.12588"
},
{
"id": "2303.17580"
},
{
"id": "2305.15334"
},
{
"id": "2307.16789"
},
{
"id": "2304.09842"
},
{
"id": "2305.11206"
},
{
"id": "2302.07842"
}
] |
2306.05424
| 38 |
Figure 5: Creative and generative tasks. Illustrative examples from Video-ChatGPTâs demonstration highlighting its performance in video-based creative and generative tasks, such as crafting a story, x poem, or advertisement.
Where is this video taken from? \) CY What place/landmark is shown in â the video? i i i . ij Pd This video is taken in New York City, 4 especially in the vicinity of the âat Statue of Liberty. The statue is shown in the background, and the video also shows the city skyline in the background. \) Describe the video. The video shows a person cutting up and â~*~ peeling a red pepper, and then cutting it | into slices. They then cut the slices âak into smaller pieces and place them on the baking sheet. How many slices are on the baking sheet. \} There are four slices of red pepper on ~*~ the baking sheet. 2 my
Figure 6: Spatial understanding tasks. The figure depicts examples from Video-ChatGPTâs demonstration, emphasizing its capability in video-based spatial understanding tasks, including identifying renowned locations or counting the number of objects in a scene.
|
2306.05424#38
|
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
|
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
|
http://arxiv.org/pdf/2306.05424
|
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan
|
cs.CV
| null | null |
cs.CV
|
20230608
|
20230608
|
[
{
"id": "2103.07461"
},
{
"id": "2302.13971"
},
{
"id": "2109.08472"
},
{
"id": "2303.05657"
},
{
"id": "2212.00280"
},
{
"id": "2305.06355"
},
{
"id": "2206.08155"
},
{
"id": "2304.10592"
},
{
"id": "2301.12597"
},
{
"id": "2005.14165"
},
{
"id": "2305.16355"
},
{
"id": "2212.03191"
},
{
"id": "2205.01068"
}
] |
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