CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Authors: Guohao Li
Hasan Abed Al Kader Hammoud
Hani Itani
Dmitrii Khizbullin
Bernard Ghanem
Venue: NeurIPS 2023 poster (2023)
Keywords: Communicative Agents
Large Language Models
AI Society
Role-Playing
Society of Mind

Abstract: The rapid advancement of chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming. This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents, and provides insight into their “cognitive” processes. To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named role-playing . Our approach involves using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of a society of agents, providing a valuable resource for investigating conversational language models. In particular, we conduct comprehensive studies on instruction-following cooperation in multi-agent settings. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond: https://github.com/camel-ai/camel.

CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
attps://wwe camel- wwl.aaiorr... org org
Guohao Li"
Hasan Abed Al Kader Hammoud"
Hani Itani"
Dmitrii Khizbullin
Bernard Ghanem
King Abdullah University of Science and Technology (KAUST)
Abstract
The rapid advancement of chat-based language models has led remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming, This paper explores the potential of building scalable techniques to facilitate au- tonomous cooperation among communicative agents, and provides insight into their "cognitive" processes. To address the challenges of achieving autonomous cooperation, propose novel communicative agent framework named role- playing Our approach involves using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of society of agents, providing a valuable resource for investigating conversational language models. In particular, we conduct com- prehensive studies on cnstruction-oolloinng cooperation in multi-agent settings. Our contributions include introducing novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabili- ties of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond: https:/ : /ttp//gittti thub. com/ camel- ai/ camel.
Introduction
"What magical trick makes us intelligent? The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle."
-Marvin Minsky, The Society of Mind, p. 308
Confronted with the complexities of real-world tasks, solving them often requires multiple steps. The rapid progress of chat-based large-scale language models (LLMs) has yielded remarkable achievements complex task-solving [82, 84, 116, 89, 5, 10, 122, 13]. Nevertheless, noting that their success is heavily reliant on human input to guide the conversation in the right direction. This reliance necessitates users to provide relevant and precise prompts based on their intentions and the chat agent's 's feedback. This can be challenging, time-consuming, and sometimes impossible. Crafting effective prompts often demands deep understanding and expertise particular domain of knowledge. Consider individual who lacks trading expertise; they would find difficult to create suitable prompts for directing a chat agent to develop trading application. This predicament is raising a crucial question: can replace human intervention with an autonomous communicative agent capable of steering the conversation toward task completion with minimal human supervision? To tackle this issue, it is crucial to conduct more research exploring the potential,
worth
Equal contribution
37th Conference on Neural Information Processing Systems (NcurIPS 2023).

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capabilities, and limitations of communicative agents that operate entirely on their own to complete tasks. Understanding how multiple agents interact with each other is important for anticipating the future of artificial intelligence. The dynamics of collaborating or competing agents play key role in determining the success of Al systems [6, 26, 27, 84, 99, 9, 10].
This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents and provide insight into their "cognitive" processes. Several challenges arise when asking a society of agents to autonomously cooperate on completing tasks. Examples we encountered in our preliminary analysis include role flipiin,, assistant repeating instructions, flake replies, and infinite loop of messages. Therefore, it is critical to investigate ways to align these models with human intentions and to explore means enabling their effective cooperation. To address these issues, we propose novel cooperative agent framework named role-playing to automate cooperation between communicative agents. Specifically, our proposed approach involves using role-playing with inception prompting to autonomously guide the communicative agents toward task completion. Only a preliminary idea is needed from human to guide the conversations toward complex task-solving.
Our library, which we make publicly available, provides modular functionality, and includes imple- mentations of different agents, examples of well-crafted prompts, and data explorers. We hope our library serves as a ground for future research in various areas such as multi-agent systems, cooperative AI, game theory simulations, social analysis, AI ethics, AI alignment, and beyond. In addition, our role role-playing method provides a highly scalable way to generate conversational data for studying the behaviors and capabilities of chat agents. We showcase how role-playing can be used to let chat agents communicate with each other for task completion and record their conversations for behavior analysis and capability understanding. In particular, we consider two cooperative scenarios of role-playing and generate two large conversational, task-oriented, and instruction- cettucctioooollowingg following datasets: A/ Society and Code. We also use our framework to collect two single-turn question-answer datasets, Math and Science, for LLM ability emergence study. Furthermore, we generate Misalignment dataset that is simulation of possible malicious applications which demonstrate the potential risks of an unaligned autonomous agent system. The datasets offer valuable resource for investigating conversational language models, enabling them to comprehend and react to human language more effectively. Fur- thermore, our role-playing offers a scalable method of creating conversational data, which can potentially enhance the development of more advanced language models. We show that solutions derived from our role-playing framework outperform those generated in a single shot by gpt-3. 3.t-3.5-turt turbo [82] in both GPT4 and human evaluations. We also study knowledge emergence in LLMs by fine-tuning LLaMA [117] progressively growing datasets generated through our framework. Additionally, we evaluate our code generation capabilities through benchmarking our final model on HumanEval [18] and HumanEval" + [69].
prrruction-follwiinn
Contributions. Our contributions are fourfold: (1) We introduce a novel cooperative agent frame- work, role-playing, that allows communicative agents to collaborate autonomously toward completing tasks while requiring minimal human intervention; (2) Our framework offers a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems. It illuminates the challenges of achieving autonomous cooperation, and provides strategies for addressing them. We showcase the potential power of multi-agent collaboration for complex-task solving; (3) We demon- strate the significant emergence of LLM training abilities by utilizing the datasets we have collected from simulating four distinct agent collaboration scenarios; (4) We have open-sourced our library, containing implementations of various agents, data generation pipelines, data analysis tools, and collected datasets, to support research on communicative agents and beyond.
2 Related Work
Communicative Agents. Communication between agents has been studied for a long time [76.
771. There are many ways to facilitate communication between agents, and with agents [29, 90,
97]. Among these, natural language considered the most natural form of communication [97]. By enabling agents to function as communicators themselves, they become capable of solving complex tasks [113, 85, 72, 3, 30, 111, 79, 41, 28, 102, 80, 106, 35, 49, 2, 51, 1, 55, 50, 65, 92]. Communication between AI agents can occur in competitive setting [115, 108] or cooperative setting [40, 27, 11, 137, 70]. Cooperative Al refers to artificial intelligence systems that are designed to work together with humans and other AI systems to achieve common goals [24, 125]. Cooperative Al systems take into account the needs and capabilities of other agents in the system and actively seek to collaborate and coordinate their actions with them, which has many potential benefits, including

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increased efficiency, improved decision decision-making, and the ability to tackle complex problems that are beyond the reach of any single agent. However, designing effective cooperative Al systems is still an active area of research, as it requires addressing a range of technical, ethical, and social challenges [27]. Our work enables communicative agents to engage in conversation and cooperate with each other to solve assigned tasks. The agents, each assigned a distinct role, are expected to apply their expertise and knowledge to solve their common task.
Instructional LLMs and Prompt Engincering. LLMs are trained on diverse text data and excel in text completion, with various downstream NLP applications [12, 22, 47. 131, 117]. However, InstructGPT suggests that LLMs may not align with user intent, proposing reinforcement learning from human feedback (RLHF) [23] and Instruction Fine-Tuning (IFT) [121] to improve LLMs' relevance and appropriateness to user instructions. Special types of instruction or prompting methods such as srain-of-Thoughe
(CoT) [123], sero-shot-CoT [61], and ReAct [ [126], have recently been developed to enhance the performance of LLMS on reasoning. arithmetic and decision making tasks [134, 118, 52, 73, 31, 103, 43, 64, 132, 46, 133, 105, 128, 25, 81, 109]. These techniques underpin the impressive capabilities of recent dialogue LLMs [106. 116, 36, 9, 82, 13], which aim to simulate human-like conversations and provide personalized and interactive experiences for users, exhibiting the behavior of conversational Al agents [33]. However, generating instruction datasets is a crucial challenge in building instruct-bases based LLMs, with existing datasets ranging from crowdsourced to generated. leveraging previously crowdsourced NLP datasets is a less labor-intensive curation approach [121, 71, 78, 53]. LLMs have been explored for data generation in [101, 63, 68. 114], and Self- [119] proposes a semi-automated process for instruction instance generation. Unnatural-Instrucee [48] collects instruction instances by prompting language model with only three seed examples and paraphrasing the generated instances to expand the dataset. There is also a large chunk of work that has proposed methods for automatic dataset creation [67, 57, 19, 75, 20, 98, 59, 96, 129, 62, 130, 86, 8). Another important challenge is prompt engineering. The quality of the prompt used to guide LLMs significantly affects its performance [91, 12, 66]. While LMS pre-trained on large data can implicitly learn tasks with few-shot prompting, hand-crafted prompts may not always suffice. Automated prompt generation methods have been proposed, such as gradient- gradient-guieed search [104], mining- based and paraphrasing- based techniques [54], a meta- peetapprompt [93], and automatic instruction selection and generation [136]. In this work, we introduce a conversational LLM auto- auto-prompting method called Inception Prompting, which enables agents to prompt each other to solve tasks through Role-Playing, The Al user continuously provides instructions to the Al assistant for task-solving. This enables us to save the streaming instruction-solle soluuiinn pairs and create diverse, instructional, conversational, and task-oriented datasets. These datasets can be used to analyze the behavior and capabilities of LLMs and for future research for fine-tuning LLMs with conversational instructions.
instruction instances
available in [120], while
AI Alignment. AI aligmment is a field that aims to ensure that AI systems adhere to their intended goals, interests, and values, as envisioned by their designers [4, 39, 110, 32, 38, 74, 10]. The first attempt AI alignment was made through the "Three Laws of Robotics," which was introduced by Isaac Asimov in his science fiction stories [6]. Developing aligned AI systems is crucial for achieving desired objectives while avoiding unintended consequences. Research in Al alignment focuses on discouraging AI models from producing false, offensive, deceptive, or manipulative information that could result in various harms [56, 112, 42, 37]. Achieving high level of alignment requires researchers to grapple with complex ethical, philosophical, and technical issues. We conduct extensive experiments to study different role-playing situations, which probe the alignment of LLMs.
3 Methodology
In this paper, we focus on studying communicative agents under cooperative settings where they share common interests. In particular, we study the assistant-user scenario, where preliminary idea is given at the start. Agents will conceptualize the idea into a specific task and complete autonomously through conversations.
3.1 Role-playing Framework
"What's 's the most resilient parasite? An Idea. A single idea from the human mind can build cities. An idea can transform the world and rewrite all the rules. Which is why I have to steal it."
-Dom Cobb, Inception
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Human Input
Med axdyyc enficuak builing
Idea: Develop trading bot for the stock market
Task Specifier
sentient analyet and stcce andiigg we can uae pio
2 leotiis on for sentment andiyate
Specified Task: Develop a trading with sentiment analyssis tool that can monitor social media platforms posltive negative comments about particular stock, and execute trades based on sentiment analysis results
Teckedal hooe Beakee opea conerntlprongg
Al User
Human User
ond yiaiea
Role Assignment:
Nost ingpent
Al Assistant:
Instrctioon Import recveury libaries in Python.
Python Programmer
User: Stock Trader
Al Assistant
Role Playing Session
Figure I: CAMEL Role-Playing Framework. Our role-playing setup starts with the human user having an idea they want to implement, e.g. develop a trading bot for the stock market. The roles involved in this task would be an AI assistant agent who is a python programmer and an AI user agent who is stock trader. The task is made more specific using our task specifier agent, leading to a well-defined task for the assistant to solve. Both AI user and AI assistant are provided with the specified task, after which they collaboratively communicate by chatting with each other in an catructtio-followiie fashion to solve the specified task.
Our proposed framework is a novel role-playing approach for studying multiple communicative agents. Specifically, we concentrate on task-oriented role-playing that involves one Al assistani and one Al user. After the multi-agent system receives a preliminary idea and the role assignment from human users, a task-specifier agent will provide a detailed description to make the idea specific. Afterwards, the Al assistant and AI user will cooperate on completing the specified task through multi-turn conversations until the AI user determines the task is done. The AI user is responsible for giving instructions to the AI assistant and directing the conversation toward task completion. On the other hand, the Al assistant is designed to follow the instructions from the Al user and respond with specific solutions. The whole role-play pll-pplaing framework depicted in Figure 1.
Human Input and Task Specifying. The role-playing session will be instantiated from an idea and selected roles by humans. As an example in Figure 1, human has preliminary idea to develop a trading bot for the stock market. Humans may or may not have the knowledge about how the idea can be realized. What is needed is only designate the potential roles that can implement the idea. For instance, a Python Programmer could collaborate with a Stock Trader to realize the idea of developing a trading bot for the stock market. After the idea and roles are determined, the task specifier agent will brainstorm specific task that the AI Assistant role can help with the AI user role to complete based on the input idea. An example of a specified task in this scenario could be: develop a trading bot with a sentiment analysis tool that can monitor social media platforms for positive or negative comments about a particular stock, and execute trades based on sentiment analysis results. The main motivation for introducing task specifier is that conversational agents usually require a concrete task prompt for realizing the task which might be challenging or time-consuming for a non-domain expert. Therefore, the task specifier agent serves as enhanced imagination module for the idea implementation. Please note that, when studying our framework at a large scale for AI society and Code scenarios, we generate roles and ideas automatically by prompting LLMs instead of relying on human inputs. For our generated Math and Science datasets we generated problem topics, subtopics, and problems automatically by prompting LLMs.
AI Assistant-User Role Assignment. After the task specification, The AI assistant role and the AI user role will be assigned to the user agent and the assistant agent correspondingly to complete the specified task. In practice, a system message is passed to each agent declaring their role. We refer to the assistant system prompt/message by PA and that of the user by Pu. The system messages are passed to the agents before the conversations start. Let Fi and F2 denote two large-scale auto- regressive language models [82]. When the system message is passed to those models respectively, we

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obtain A FPA und + Ff which are referred to as the assistant and user agents respectively. In Figure 1, the AI assistant and the Al user are assigned the roles of a Python Programmer and Stock Trader at the beginning of the role-playing session respectively. The Al user serves as task planner, engaging in interactive planning to determine feasible steps for the Al assistant to execute. Meanwhile, the Al assistant acts as a task executor, offering solutions, executing planned steps, and providing responses to the Al user.
Conversation Towards Task-Solving. After the role assignment is completed, the AI assistant A and Al user U/ will collaborate in an instruction- following manner to accomplish the task. In the Al assistant-user scenario, the Al user is responsible for providing instructions, and the assistant expected respond with a solution that fulfills the instructions. Formally, we denote the user instruction message obtained at time by I, and the assistant solution by S.. The set of conversational messages obtained up until time denoted by Equation (1) shown below:
Mr So),, (I,, 5,)} S,))
At the next time step, 1. + 1, the AI user // takes the historical conversation message set M, and provides a new instruction I,+1, as shown in Equation (2). The produced instruction message I1+1 is then passed, along with message set M,, to the AI assistant A. The AI assistant will then respond with solution, denoted by S in Equation (3):
T=1=
= (MMt)
(2)
St. + = I(MI,
After obtaining the solution S(+1 to the instruction I++1, the message set is updated using Equation (4) to obtain MM41:
M41 + M, U S441)
(4)
Note that the formulation above not only models AI-AI communicative scenarios, but it can also be easily extended to model human-Al communication or communication between more than two agents. Specifically, we can use message-passing graphs to model communication between an arbitrary number of agents. In Figure essential Python libraries for sentiment analysis and stock trading by instructing the Al assistant through conversations. This example is drawn from our experiments, and the entire conversation is available in the Appendix.
observe that the Al user initiates the installation and import of
Pritic-In-The-Log To enhance the controllability of the role-playing framework, we introduce a critic agent capable of selecting proposals from or providing feedback to the role-playing agents. This enables tree-search-like like decision-making for task-solving. In practice, the critic be either an Al agent or human. The detailed implementation and case studies can be found in the Appendix.
3.2 Inception Prompting
Since prompt engineering is crucial to our role-playing framework, this section delves deeply into our prompting techniques. Our prompt engineering occurs solely at the beginning of role-playing, for task specification and role assignment. Once the conversation phase commences, the AI assistant and AI user prompt each other automatically in a loop until termination. As such, we refer to our technique as Inception Prompting. Our Inception prompt consists of three prompts: the task specifier prompt Pr, the assistant system prompt PAA, and the user system prompt Pv. As an example, we consider the inception prompt of the AI Society scenario. The templates for these prompts of AI Society role-playing are shown in Figure 2. The task specifier prompt contains information about the roles of the Al assistant and AI user in the role-playing session. Therefore, the task specifier agent can take a preliminary task/idea as input and generate a specific task using imagination. The AI assistant system prompt PA and the AI user system prompt Pu are mostly symmetrical and include information about the assigned task and roles, communication protocols, termination conditions, and constraints or requirements to avoid unwanted behaviors. The prompt designs for both roles are crucial to achieve autonomous cooperation between agents. It is non-trivial to engineer prompts that ensure agents act in alignment with our intentions. We take the prompt templates from the A/ Society in Figure 2 as an example to explain our key design choices. The prompts used for the Code scenario follow similar sprint as the AI society scenario, but with some additional engineering related to programming languages. More details in the Appendix.
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AI Society Inception Prompt
Task Specifier Prompt:
<ASSISTART_ ROLE halp KUSER_ complate: <TASK>.
Please nake it apeciric, Be creative and inaginative.
Please reply with the specified task LINIT> words or less, not add anything else.
Assistant System Prompt:
User System Prompt:
Kever forget
Never forget
<USER_ BOLE> and <ASSISTANT ROLE>>
AISISTTATT_ROLE and un RISER_ Never flip roles! Kever instruct na!
Never flip roles! You will alvays instruct ne. share
collaborating successfully
complate task.
must help you complete Here is the task: <TASK> Kever forget our tack! You must instruct ne based on ay expertise and your needs to complete the task ONLY in the following two ways:
collaborating to successfully complete task.
You must help task.
complete the
Hare the task: <TASK>. Never Iorget our task!
Instruct with Ine tetucttcin: <YOUR, INSTRUCTIO) Input: INPUT_
input:
you based your
expertise and needs to complete the task.
2. Instruct vithout any input:
Instruction: INSTRUCTTTT Input: None
The "Instruction" describes a task or quastion. The paired "Input" providea further context information You nust write epecific solution requested "Instruction".
must give you one instruction at time.
appropriately completes requested instruction.
You must give ne one instruction at time.
write responsa appropriataly completes honestly if you cannot perform the requested instruction. instruction on due to physical, noral, I must declina your instruction honestly cannot parform
maat declina my instruction
your capability and explain the reasons.
legal
the instruction due physical, noral, legal capability and explain the reasens. You should instruct Now must start to instruct using the two vays described above.
say should alvays start with:
completed,
questioas.
Solution: SOLUTTOD
Do add anything clse other than your instruction and the optional corresponding input!
SOLUTION>
Keep giving instructione necessary inputs you think the task is completed.
specific, and provide preferable
implementations and examples task-nolving.
completed, you only roply with single
word TAAKEL_TASK_DOOO DONE>.
Alvays end SOLUTIOO with:
Never say <CAMEL, TASK_ DONE> unleas my responsea have solved your task.
Next request.
Figure 2: Inception Prompt of Al Society Role-Playing. This shows the task specifier prompt, assistant system prompt, and user system prompt which are used for studying the Al society scenario.
Prompt Engineering, To delve deeper into the details in Figure 2, we start by chunking the various parts of the Al assistant system prompt PA shown below:
Never forget you are <ASSISTANT_ROUE RRLE>> and I <USER_ ROLE>, This assigns the chosen role the assistant agent and provides it with information about the user's role.
Never flip roles! Never instruct me! This prevents agents from flipping roles. In some cases, we have observed the assistant and the user switching roles, where the assistant suddenly takes control and instructs the user, and the user follows those instructions.
You must decline my instruction honestly if you cannot perform the instruction due to physical, moral, legal reasons or your capability and explain the reasons. This prohibits the agent from producing harmful, false, illegal, and misleading information.
Unless say the task is completed, you should always start with: Solution: -YOUR_SOLUTION provide preferable implementations and examples for task-solving. encourages the assistant always responds in consistent format, avoiding any deviation from the
<YOUR_SOLUTION> should be specific, and
This

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structure of the conversation, and preventing vague or incomplete responses, which we refer to as flake responses, such as will do something".
Always end your solution with: Next request. This ensures that the assistant keeps the conversation going by requesting a new instruction to solve.
For the Al user system prompt Pu, we strive to maintain as much symmetry as possible with respect to the Al assistant system prompt. Apart from the opposite role assignment, the user system prompt differs from the assistant prompt in the following ways:
following two ways: 1. Instruct with a necessary input: ...; 2. Instruct without
must instruct me to complete the task ONLY
any input: the generated instruction-solution pairs to be easily used for fine-tuning LLMs.
This follows the typical data structure of instruction-follovingg which allows
Keep giving me instructions and necessary inputs until you think the task is completed. When the task is completed, you must only reply with a single word THAMEL_TASKDOOOE
We introduce an end-of-task token, namely, <CAMEL_TASKE TASK___AA____ TEAAEEL_AS__DON>>>>>>> DONE>, This token is used once the user believes the task done. This ensures that the chat is terminated when the user is satisfied. Without doing so, the agents might fall into a chatting loop where they keep on saying "thank you" to each other or "goodbye" indefinitely.
Experiments
In this section, we will discuss the various experiments that we conducted to arrive at our final design choices. Specifically, we will examine the interesting observations, challenging issues, and several examples we have encountered while enabling agents to communicate with each other under different prompt design choices to achieve autonomous cooperation. In our experiments, we employed two gpt-3.5-turbo agents, referred to LLM agents for simplicity, with Inception Prompts, as described in Section 3.2, to simulate assistant-urs user cooperation. For our analysis, we set our attention on AI Society setting. We also gathered conversational data, named CAMEL Al Society and CAMEL Code datasets and problem-soluee coolutioo pairs data named CAMEL Math and CAMEL Science and analyzed and evaluated their quality. Moreover, will discuss potential extensions of framework and highlight both the risks and opportunities that future Al society might present.
Data Generation Prompts of AI Society
AI Society
Assistant Role Generation Prompt:
User Role Generation Prompt:
You are helpful assistant that can play many roles. Now please list ROUES different roles that you can play with your expertise in diverse fields. Sort then by alphabatical order. No explanation required.
Please list SHUMROLESS noet counon and diveree groupe internet users occupations. Use singular form. No explanation. Sort then by alphabotical order. No explamation required.
Task Generation Prompt:
List SJUM_AASSS> diverse tacks that CASSISTANT_ROLA assist <USER_AOLE> cooperatively to achieve togother. Be concise. Be creative.
Figure 3: Data Generation Prompts. In order to maintain scalable approach data parameters are generated using an LLM model to reduce human involvement in the generation process. The generation prompts for both AI Society dataset summarized in this figure.
4.1 Role-Playing for AI Society
To create our AI Society dataset, we have developed scalable approach that follows a series of steps. Firstly, we prompt the L.M agent to generate possible roles for the assistant and the user. We achieve this by providing the LLM agent with specific prompts designed to elicit these roles. Next, we ask the I.M agent to generate a range of possible tasks that can be solved through collaboration between the assistant and user roles generated previously. After generating range of possible tasks as described
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in the previous step, we then use the task specifier prompt passed to the LLM agent to make the task more specific. The prompts for assistant role generation, user role generation, and task generation are shown in Figure 5 (A/ Society). For our Al society dataset, we generated 50 assistant roles, 50 user roles, and 10 tasks for each combination of roles yielding total of 25,000 conversations. The generated assistant roles and user roles for Al Society well as details about the generation of Code, Math and Science datasets can be found in the Appendix.
Challenges and Observations. In this section, we explore the four main challenges that we identified during our analysis of the generated datasets. Our observations shed light on some interesting aspects of cooperative Al and the difficulties that arise in its development.
Role Flipping: One challenge we encountered was role flipping, where the assistant and user switch roles during the conversation. This issue typically arises when the assistant starts providing instructions commands instead of following the user's prompts, which can lead to confusion and a reversal of roles. To avoid role flipping, is erucial for the assistant not to ask questions, as this can also contribute to the problem.
Assistant Repeats Instruction: Another challenge that we observed was the assistant simply repeating the user's instructions without any role flipping occurring.
Flake Replies: We also observed instances where the assistant agent responds with flake reply, often taking the form of "I will...". These messages do not contribute to the task at hand, as the assistant promises to take action but ultimately fails to follow through.
Infinite Loop of Messages: An interesting challenge that we encountered was when the assistant and user engage in an infinite loop of meaningless conversation, such repeatedly thanking each other or saying goodbye without progressing the task. Interestingly, some cases, the assistant and user are aware that they are stuck in loop, but are unable to break out of it.
The Appendix shows examples of each of the four challenges discussed above. Overall, our observa- tions highlight the complexity of cooperative Al development and the need for continued exploration and innovation to overcome the challenges face. By identifying these issues, we hope to contribute to the development of more effective and engaging cooperative Al systems.
Termination Conditions. The conversation between the assistant and user agents is designed to follow specific format to ensure consistent and accurate data generation. To ensure that both the user and assistant adhere to their respective roles and responsibilities, certain conditions have been set in place to terminate the chat if necessary. These conditions are outlined below:
User No Instruct: the user does not instruct the assistant for rounds, conversation is ended.
Assistant Instruct:
If the assistant provides an instruction to the user, indicates role reversal, and the conversation is terminated.
End of Task Token: If the user believes that the task has been solved, they are expected to say <CAMEL_ TASK_ DONE> to signify the completion of the task. Once this message is received, the conversation is terminated.
TASK_DONE>
1.ot-3.5-taae 1..33.55uubr turbo has limitation on the number of tokens, the conversation is terminated if either the assistant or the user reach the token limit.
Assistant&Üser Token Limit: Given that gpt-3. 3.
Maximum Number of Messages: To keep the cost of generated chats in check, we have set a maximum limit of 40 messages. This limit guarantees a long enough conversation between the user and assistant while also ensuring that the data generated is not too costly to produce. The cost grows quadratically with the length of the conversation, making it essential to set limit.
5
Evaluation
5.1 Agent Evaluation
In order to assess the performance of CAMEL (Cooperative Role-playing Communication), we conduct two types of evaluations: (1) Human evaluation, and (2) GPT4 evaluation. We randomly select 100 tasks from our AI Society dataset for evaluation and 100 tasks from our Code dataset. Then, we employ the GPT4 model to summarize the content of the CAMEL conversation based
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solution, presenting a consolidated final solution. Particularly. GPT4 is used since it possesses a larger token limit which suitable for summarization. Summarization also makes CAMEL agents' solution undetectable by its format, allowing for more fair comparison. Subsequently, this solution compared with a single-shot solution generated by the gpt-3. 1.--3.5-turbr model for the same task. Sample tasks are provided in the Appendix.
Human Evaluation. For this evaluation, we present both the CAMEL summarized agent solution and the gpt-3. 5- single-shot solution side-by-side to human participants. The identity behind each solution is not revealed. Participants are then asked to vote on whether one solution is superior to the other or if they are equally good. A total of 453 responses were collected during this evaluation. Note that, human evaluation only done for AI Society, as assessing code is generally harder for humans (without rumning the code).
GPT4 Evaluation. We engage GPT4 agent evaluate the effectiveness of Model (CAMEL Agent solution) versus Model 2 (gpt-3. 5-turbo single-shot solution) for each task. More specifically, we prompt GPT4 score and decide which solution of the two solutions is better.
Results. The summarized results of each evaluation are outlined in Table I which showcases that the CAMEL solution outperforms gpt-3. Ept-3.5-turbo single-shot solution in both the human evaluation and the GPT4 evaluation by a big margin. It is also worth noting that both human evaluation and GPT4 evaluation are highly aligned.
Table I: Agent Evaluation Results: Results of the evaluations of the CAMEL agent against gpt-3. 1..-3..5tte 5-turbo using both human evaluators and GPT4 consistently show that utilizing multi- agent cooperative approach is more effective than gpt- 5- t-turoo's 's single shot solution.
Dataset
Evaluation Type
Draw
gpt-3,5-turbo Wins CAMEL Agents Win
Human Evaluation
13.3%
10.4%
76.3%
AI Society
GPT4 Evaluation
4.0%
23.0%
73.0%
Code
GPT4 Evaluation
0.0%
24.0%
76.0%
5.2 GPT4 for ChatBot Evaluation
In this section, we progressively fine-tune a LLaMA 7B model on our generated datasets. By progressively incorporating diverse datasets like AI society, code, math, and science, we expect fine-tuned model to demonstrate the ability to develop an increasingly sophisticated understanding of these domains.
We initially start by training on Al society dataset, which aims to let the model learn about human interactions and societal dynamics. As additional datasets were introduced, such as code, the model gained knowledge of programming logic and syntax, enabling it to generate coherent and executable code snippets. The inclusion of the math dataset further expanded the model's capabilities, allowing it to solve complex equations, reason about abstract concepts, and perform precise calculations. Finally, exposure to the science dataset broadened the model's understanding of scientific theories, empirical observations, and experimental methods. The emergence of model capabilities is measured by evaluating the quality of the model responses, before and after training on the new domain, on a set of questions of varying difficulties from each domain. More precisely, the model is tested on 20 AI Society related tasks, 20 coding tasks, 20 math tasks and 60 science tasks.
Those results are highlighted in Table 2 where we see that each time we add a dataset, the model performs better on the incorporated domain. Note that to measure the quality of the models' responses, we follow the evaluation from Section Т, which involves prompting a GPT4 agent to score and decide which solution is better. It worth noting that an improvement on other domains is also observed in some cases such as when we train on Code we improve on Science. This is because our Code dataset contains problems that solve tasks in particular domains which include scientific domain. Similarly, training on AI Society improves code as AI Society contains the role of a "programmer" and hence coding related conversations. Finally, note that the draws observed in LLaMA-7B vs AI Society in Math reflects equally bad solutions compared to the draws observed in AI Society + Code Math vs AI Society + Code + Math Science where the draws are equally good solutions. This progression from AI society to code to math to science highlights the potential of Al models to acquire versatile

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and adaptable knowledge base, paralleling the way humans gain expertise in diverse subjects. Sample tasks are provided in the Appendix.
Table 2: Emergence of Knowledge. By progressively fine-tuning L. LLMM on datasets from different domains, observe the emergence of knowledge the model transitions from Al society code, math, and science. This finding is indicated by the fact that Model almost always performs better than Model 1, especially the added dataset.
Model
Model
Dataset
AI Society
Code
Muth
Science
Al Society
Code
Muth
Science
Model
Model
Society
14
Code
Math
Science
AI Suciety
8
Code
Math
Science
40
Society
Code
10
Science
49
Society
Code
Math
10
Seience
AT Soclety
20
Code
Math
20
Science
60
5.3 HumanEval(+)
Table 3: HvmanEval for Various Models. We test our CAMEL model, which a LLaMa-7B fine-tuned on all our datasets (AI Society, Code, Math, Science) HumanEval and HumanEval* benchmarks, where we show competitive pass@k scores with LLaMa-7B and Vicuna-7B.
HvmanEval
HumanEval*
pass [%]
==1
k
100
k ==
k 100
gpt-3, 5-turbo
69.4
94.0
61.7
89.8
L.L.MA-BB
10.5
36.5
Vicuna-7B
11.0
42.9
34.7
CAMEL-7B
14.0
57.9
12.2
50.0
To evaluate the coding task-solving capabilities of our CAMEL model, specifically the LLaMA- 7B fine-tuned on our comprehensive datasets, we rely on HumanEval [18] and HumanEval* [69]. The results, depicted in table clearly demonstrate the remarkable performance of CAMEL. surpasses not only the LLaMA-7B model but also Vicuna-7B [21] by a big margin. These findings underscore the critical role played by the generated datasets in enhancing LLaMA's ability tackle coding-related tasks.
Conclusion
In this paper, we explore the potential of autonomous cooperation among communicative agents and propose novel cooperative agent framework named role-playing Our approach enables communicative agents to collaborate autonomously toward completing tasks while requiring minimal human intervention, leading better solutions per thorough evaluations. Through our analysis, we show that achieving autonomous cooperation is challenging due to issues like conversation deviation, role flipping, and termination conditions. Our framework offers scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems and provides strategies for addressing these challenges. Furthermore, open-sourced library includes implementations of various agents, data generation pipelines, data analysis tools, and collected datasets, to support research communicative agents and beyond. Our contributions offer valuable insights into the future of large language artificial intelligence models and cooperative Al systems.
7 Acknowledgements
This work was supported by SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence EDAIA-KAUST AI).
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Reviews:
[{"review_id": "NSWGQJbZhz", "reviewer": "Reviewer_wYFm", "summary": "This paper attempts to address a dilemma in leveraging large-language models (LLMs) for solving complex tasks in a collaborative setting: the question of oft-needed human intervention in the equation. More specifically, the authors have come up with an intuitive and novel cooperative agent framework called role-playing that supports effective task completions by collaboration dialogues between agents (LLMs) without extensive human interventions except at inception. Their framework also offers a scalable way to investigate and refine the collaborative capacities of multi- agent systems and they provide detailed analysis and resolution strategies for challenges that come up in such a to- and-fro LLM-instruction scenario. They evaluate their agents exhaustively with state-of-the-art LLMs like GPT 4 as well as with human intelligence. Moreover, they demonstrate task-specific emergence capabilities of smaller-sized LLMs like Llama using their generated user-instruction datasets for various scenarios and domains.\n\nContributions:\nDatasets/Libraries\n1. Their publicly available library provides modular functionality and includes implementations of different agents, examples of well-crafted prompts, and data explorers.\n2. Two large conversational, task-oriented, and instruction-following datasets: AI Society and Code.\n3. Math and Science dataset (QA) and Misalignment dataset (contains simulations of potential risks of such an\nuncontrolled autonomous system)\n4. These datasets will help investigate other larger language models, allowing such LLMs to communicate more\neffectively with human agents.", "questions": "(Also suggestions)\n1. Sec 3.2 makes note of cases of role-switching being observed between the user and the assistant. Are those cases of LLM hallucination anyway correlated with the framing of the prompt? Does it affect certain tasks more than others or is it observed usually at specific points in the sequence of collaborative dialogues?\n\n2. The authors precisely point out challenges such as instruction repetition, flake replies, and infinite loops of messages with examples in the appendices. However, one way of improving the analysis of the paper would be to have a plot/diagram with more details about the distribution of such cases (frequency, places where they tend to occur in the sequence of dialogues, etc). Also, what methods were used to stop/terminate such cases of looping/role-flipping, was it human intervention or did the authors use flags similar to the task termination conditions?\n\n3. On section 5.2, which explains the methods for evaluating ‘emergence’ capacities in smaller LLMs like Llama, what was the motivation behind the sequence of various domains (math, science etc) being a specific way and not any other? Moreover, Table 1 can also benefit from some clarification about what exactly Model 1 and Model 2 are. Although the authors have pointed to sec 5.1 for the evaluation in sec 5.2, it is unclear which one is Llama variant. Also, there might be a case for further experiments with other relatively smaller-sized instruction-based LLMs like T0 [3], FLAN [2] or InstructGPT [3]", "limitations": "The authors have explicitly mentioned the limitations and have provided reasonable strategies to avoid any harmful (pricing/social or otherwise) consequences of their work. However, the paper might benefit from a cost- compute analysis in generating the collaborative datasets using LLMs, perhaps as a paragraph in the main paper.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "The proposed method (inception prompting) is intuitive, novel, and well-motivated for a novel collaborative task-solving using LLM-agents.\n\nThe paper is mostly easy to follow. For instance, Fig. 2 is pretty detailed when it comes to roles and task assignments.\n\nThe supplementary material, appendices, and the libraries provided can be crucial for future works in this direction.\n\nThe methods section is scientifically sound with effective strategies being discussed for resolving the unaligned idea flows from a role perspective.\n\nThe proposed framework can be used to evaluate collaborative problem-solving in crucial domains like classroom learning and education.", "weaknesses": "1. Role/task alignment: For the specific task of building an app for stock trading via analyzing sentiments of certain stocks, it seems that the role assigned to the AI user (stock trader) and the message it generates seems counterintuitive since you wouldn’t generally expect stock traders to know environment variables being the first step towards solving this specific task. Instead, a role (like say tech lead/ tech supervisor) seems more fit for the AI user in that example.\n\n2. Fig. 2 confusion: Fig.2 is a little confusing regarding which agent receives the task-specifier prompt (i.e., at the inception level). If I am reading correctly, the multi-agent scenario seems to include two LLMs, but it is not clear who received the starting ‘task-specifier’ prompt immediately. It’ll perhaps improve the readability if the authors can provide some clarity regarding this either in sec. 3.2 or Fig 2\n\n3. Long-distance memory issues: in section 4.1, the authors provide a set of termination conditions that brute- force the collaborative mutual dialogues between the agents. Is it not entirely clear what exactly the authors mean when they claim 40 to be the max-limit of messages to ensure enough length in the conversation history. Was it chosen purely from a pricing perspective? I think the paper can benefit from having some more analysis from a cost/compute perspective. Also, did the authors come across any cases where the agents tended to effectively forget the previous thread of instruction flow (thus leading to sub-optimized task results) because of long-range memory issues that tend to affect such LLM-based agents?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer wYFm:\n\n**Response to Strengths:**\nWe appreciate your positive comments on the novelty, motivation, and potential benefit for future work.\n\n**Weaknesses:**\n\n1. **Response:** We acknowledge the reviewer's observation about the role of the AI user (stock trader). The decision to use the 'stock trader' role was based on the assumption that the AI user stock trader has domain knowledge about stock trading and some basic knowledge of Python. So that the stock trader can collaborate with a Python programmer to achieve the task with its domain knowledge. We see the merit of having the suggested role of 'tech lead/tech supervisor'. Inspired by your suggestion, we propose a more realistic and aligned workflow:\n\n    Stage 1: A stock trader (AI User) collaborates with a tech lead (AI Assistant) to figure out a detailed implementation plan for developing a trading bot for the stock market.\n\n    Stage 2: A tech Lead (AI User) then collaborates with a Python programmer (AI Assistant) on implementing the obtained plan.\n\n    (See the PDF in the global response for the details)\n\n    In this two-stage approach, we see the implementation is more systematic and well-rounded, which includes the designs of Data Storage, Risk Management, Security, and even Testing and Deployment. What would be even more interesting is to introduce an automatic role-generation agent that can generate roles for a given task. We will add the above discussion in the final manuscript.\n\n\n2. **Response:** We will clarify this in section 3.2 and enhance Fig. 2 to better illustrate the inception prompts. As mentioned in 3.1 Role-playing Framework - Human Input and Task Specifying. The 'task-specifier' prompt is the system prompt for the task specifier agent which receives a preliminary idea and roles from Human input and produces a specified task. The task specifier agent acts as a brainstorming module to help with producing a specified task for the AI User and AI Assistant agents.\n\n\n3. **Response:** Our decision to limit the number of messages to 40 was mainly cost-related. Even if we provide a set of termination conditions, we still want to put a safeguard to the max-limit of the message. It is because after the task is completed the agents will provide short outputs like \"thank you\" and \"welcome\". If no safeguard is set and termination fails, the conversation will only end until it exceeds the token limit, which may end up with thousands of API calls and hundreds of USD dollars cost.\n\n    As for forgetting the previous thread of instruction flow, it is mainly related to agents asking for information that would not be able to perform since the lack of embodiment or physical information such as date, emails, files, location, etc. For instance, an AI user agent asks an AI assistant agent to book a meeting schedule in its calendar. However, the AI assistant agent does not ask for access to the AI user agent's calendar. Then the AI assistant agent will ask for the AI user agent's calendar access. However, we did not provide calendar API accesses to the AI user which cause the forgetting of the thread of instruction flow. This could be solved by providing API access to the embodiment or physical information. In the appendix (O Embodied Agent), we show an example that provides image generation API to an embodied agent which enables it to generate images.\n\n**Questions:**\n\n1. **Response:** We indeed noticed that role-switching can sometimes be task-dependent. As mentioned above, it is mainly related to agents asking for information that would not be able to perform since the lack of embodiment or physical information such as date, emails, files, location, etc. (See the response to Weaknesses 3)\n\n2. **Response:** A dataset analysis is provided in the appendix (Dataset Analysis) including some plots for the distribution of these cases. Please see Figures 9 and 10 for the analysis of the distribution of conversation termination reasons and Figure 11 for the flake message distribution. Yes, the checking is similar to the task termination conditions. Role flipping is checked when the assistant instructs the user with keywords like \"Instruction\" and looping is checked frequency of a list of words like “thank you” and “you’re welcome” etc.\n\n3. **Response:** The sequence of domains was initially ordered by the time orders of the collection which is relatively arbitrary. We will clarify the details in Table 1. Model 1 and Model 2 are both LLaMA 7B-based models which are trained on different datasets. For instance, LLaMA-7B is the vanilla model, AI Society and AI Society + Code + Math + Science are models trained on the AI Society dataset and a combination of datasets (AI Society + Code + Math + Science) respectively. Moreover, we appreciate your suggestion of comparing with other LLMs like T0 [1], FLAN [2], and InstructGPT [3].\nBelow we provide results that show the emergence of knowledge of AI Society-related concepts of a FlanT5 Model. Fine-tuning a FlanT5 on our AI Society data improves the performance of the model on Society related tasks. Additionally, we compare FlanT5 fine-tuned on AI Society data with a LLaMA-7B fine-tuned on the same data and find that they achieve very similar scores with FlanT5 performing slightly better.\n\n    | **Dataset**     | **Model 1**                | **Model 2**                     | **Draw** | **Model 1 Wins** | **Model 2 Wins** |\n    |---|---|---|---|---|---|\n    | **AI Society**  | FlanT5                     | FlanT5 (+AI Society)            | 1       | 0                | **19**               |\n    | **AI Society**  | FlanT5 (+AI Society)       | LLaMA-7B (+AI Society)          | 2       | **10**               | 8                |\n\n**Limitations: Cost-compute analysis ...**\n\n**Response:** We'll add a paragraph addressing this in the revision. Generating all four datasets (AI Society, Code, Math, and Science) cost around 10,000 USD. \n\nOnce again, sincerely thank you for your constructive feedback!"}, {"review_id": "wDN06X1NXw", "reviewer": "Reviewer_6oPa", "summary": "# Summary\n## Motivation\nCompleting tasks by human-in-the-loop is time-consuming. An alternative is to let autonomous agents cooperate to solve tasks. \n## Approach\nThis paper propose to let two LLM agents *role-play* a user and an assistant to solve tasks. \nTheir data collection approach follows the following steps:\n1. For each dataset, they generate a number of user roles, a number of assistant roles and a number of tasks of each combination. \n2. For each user, assistant, task combination, they generate the conversation by prompting both roles to iteratively generating instruction and solutions, and doing critic-in-the-loop to improve generations at each step. \n\nThey fine-tuned 7B LLaMa models on different combination of datasets. The comparison between models are judged by GPT-4, whose accuracy is validated by small sample human evaluation. \n## Results\nFirst, CAMEL (the proposed role-play method between two LLMs) works better than gpt-3.5 as a single model. \nSecond, in most cases each generated dataset brings improvement to finetuning. \nThird, on third-party code datasets HumanEval+, they show that the finetuned small models works better Vicuna. \n## Contribution\n1. This paper proposes a framework and prompt methods for solving tasks with multiple LLMs with different roles.\n2. This paper generates conversation datasets in solving these tasks, which will be helpful for future research. \n3. This paper fine-tuned smaller model and showcased the performance on an out-of-domain code generation dataset. ", "questions": "1. Section 5.1, Fig. 4. are the CAMEL agents here GPT-4 based or the fine-tuned model mentioned in 5.2? If it is GPT-4, would the comparison with get-3.5-turbo be unfair? \n2. Section 5.3, on which datasets is the CAMEL-7B trained on? All datasets or just code? ", "limitations": "Yes", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "# Originality\nUsing a team of LLMs or generally NNs to solve a task is an interesting problem, but understudied in practical applications. This paper is innovative in that the generated tasks are in general domain, but also related to the agent roles engaged in the tasks. \n\n# Quality and Clarity\nAs LLMs become accessible, the methods in this can be almost replicated by putting the prompts into the playground. This paper is clearly written, although not self-contained due to the shortage of space, e.g. the description of critic-in-the-loop model is in the appendix, though very important. \n\n# Significance\nThe results in this paper are significant, except for the unclear parts in experiments (see questions). Due to the unavailability of good metrics when comparing models for general tasks, this paper uses GPT-4 for evaluation. This practice is widely used and has various issues raised by recent papers. Validating the results by doing small sample human evaluation is a reasonable choice. ", "weaknesses": "1. The \"large scale language model society\" can be misleading. In the whole paper, the conversations are between two agents with different roles. Readers might expect more agents to participate in decision-making. \n2. It is unclear why this approach is different from hierarchical decision-making with the user as the high-level planner, and assistant as the low-level executor. The planner gives high-level instructions, while the low-level executor generates solutions. It is true that there are not many papers on LLMs for hierarchical decision making, however, there is a recent one [1]. This is not essentially a reason to reject this paper, but the authors should make the connection clearer. \n\n[1] Hierarchical Prompting Assists Large Language Model on Web Navigation", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer 6oPa,\n\nThank you for your thoughtful review and positive feedback on our paper. We're pleased to hear the originality and significance of our work is recognized. Below, we address your questions and concerns:\n\n **Strengths:**\n\n> 1. **Originality** - Using a team of LLMs or generally NNs to solve a task is an interesting problem, but understudied in practical applications. This paper is innovative in that the generated tasks are in the general domain, but also related to the agent roles engaged in the tasks. \\\n> 2.  **Quality and Clarity** - As LLMs become accessible, the methods in this can be almost replicated by putting the prompts into the playground. This paper is clearly written, although not self-contained due to the shortage of space, e.g. the description of the critic-in-the-loop model is in the appendix, though very important. \\\n> 3.  **Significance** - The results in this paper are significant, except for the unclear parts of the experiments (see questions). Due to the unavailability of good metrics when comparing models for general tasks, this paper uses GPT-4 for evaluation. This practice is widely used and has various issues raised by recent papers. Validating the results by doing a small sample human evaluation is a reasonable choice.\n\n    **Response to Strengths:** We appreciate your acknowledgment of the originality, quality, clarity, and significance of our work. We recognize that the description of the critic-in-the-loop model was limited due to space constraints, and we will strive to provide more clarity within the main body of the paper.\n\n **Weaknesses:**\n\n> 1. The \"large-scale language model society\" can be misleading. In the whole paper, the conversations are between two agents with different roles. Readers might expect more agents to participate in decision-making.\n\n    **Response:** We apologize if the term was misleading. Our focus was to demonstrate how a smaller group of agents with well-defined roles could collaboratively solve a task. These LLM agents play different roles that human society has. In the AI society dataset, we collect 50 * 50 pairs of agents with different society roles. In the appendix (P Critic-In-The-Loop), we also show a small society with three agents: a user agent as a Postdoc, an assistant agent as a Ph.D. student, and a critic agent as a Professor. We understand that readers might expect a larger society of agents, and we will work on clarifying our approach.\n\n> 2. It is unclear why this approach is different from hierarchical decision-making with the user as the high-level planner, and the assistant as the low-level executor. The planner gives high-level instructions, while the low-level executor generates solutions. It is true that there are not many papers on LLMs for hierarchical decision-making, however, there is a recent one [1]. This is not essentially a reason to reject this paper, but the authors should make the connection clearer. \\\n> [1] Hierarchical Prompting Assists Large Language Model on Web Navigation\n\n    **Response:** We agree with your comparison of our approach to hierarchical decision-making. Thanks for pointing out the missing reference [1]. This paper is indeed relevant but it came out one week after the NeurIPS submission deadline. So we were not able to cover it. The suggested paper [1] proposes a novel hierarchical prompting Actor-Summarizer-Hierarchical (ASH) for web navigation. The action is generated based on a summarized observation by a summarizer instead of the raw observation. The hierarchical modularized design reduces the heavy reasoning burden and improves performance significantly on Webshop tasks [2]. We agree there are some similarities in terms of reducing the difficulty of reasoning complex tasks in both methods. Our intention was to highlight how specific role-playing with multiple agents can lead to a more cooperative solution. We will strengthen this connection by discussing the similarities and differences with the referenced paper on hierarchical prompting and explaining our unique contribution to the field. We also found a more recent (25 Jul 2023) follow-up work WebArena [3] on using autonomous agents for web navigation to be impressive.\n\n    [2] Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan. 2022a. Webshop: Towards scalable real-world web interaction with grounded language agents. arXiv preprint arXiv:2207.01206.\n\n    [3] Zhou, S., Xu, F.F., Zhu, H., Zhou, X., Lo, R., Sridhar, A., Cheng, X., Bisk, Y., Fried, D., Alon, U. and Neubig, G., 2023. WebArena: A Realistic Web Environment for Building Autonomous Agents. arXiv preprint arXiv:2307.13854.\n\n\n**Questions:**\n\n> 1. Section 5.1, Fig. 4 are the CAMEL agents here GPT-4 based or the fine-tuned model mentioned in 5.2? If it is GPT-4, would the comparison with get-3.5-turbo be unfair?\n\n    **Response:** We apologize for the confusion. The CAMEL agents used in our experiment were based on gpt-3.5-turbo for role-playing (See line 288), not GPT-4. GPT-4 is used as a judge for the evaluation. We also use GPT-4, since it has a larger token limit, to extract the conversational solutions generated by the CAMEL agents to summarized ones, which is aiming at making the judge (GPT-4 or human) not able to tell the differences between the solutions by the formats. We will further clarify this aspect in our revision.\n\n> 2. Section 5.3, on which datasets are the CAMEL-7B trained? All datasets or just code?\n\n    **Response:** CAMEL-7B was trained on all datasets (AI Society + Code + Math + Science), not just code, which is similar to how Vicuna was trained on diverse datasets that do not include only code. We will include clearer information about this in the revised paper.\n\nOnce again, thank you for your insightful comments. They will be of great assistance as we refine our work in the final version."}, {"review_id": "bYSCE5iWQu", "reviewer": "Reviewer_Z1bn", "summary": "This paper introduces CAMEL, a role-playing framework involving two LLMs (an AI user and an AI assistant) communicating with each other to finish a specific task prompted by a human. The two agents are supposed to give instructions and provide answers respectively. On complex tasks (including AI society, code, math, and science), the proposed framework achieves better performance than a GPT-3.5-turbo single-shot baseline evaluated by both human and GPT-4. Further analysis involving fine-tuning a LLaMA model suggest the challenges and emerging behaviors in multi-agent multi-turn conversations.", "questions": "1. Is there an inconsistency in naming? I think the proposed method is named as \"role-playing\" but is also referred to as \"CAMEL\".\n2. In line 277, how do the assistant and the user know that they are stuck in a loop but are unable to break out? \n3. Can you clarify the questions raised above?", "limitations": "The authors have addressed the limitations and potential societal impact.", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper introduces an interesting framework role-play that instead of requiring delicate human prompting, two agents are auto-prompted each other (Inception prompting) to solve a task by collaboration in a multi-turn conversation. This may add values to the research community studying complicated prompting methods to solve complex tasks.\n2. Evaluation and analysis indicate challenges in multi-agent collaboration such as role flipping and conversation deviation. This can be interesting to future research involving multiple language models.\n", "weaknesses": "1. Baseline and evaluation. The proposed method, although sounds promising and conceptually novel, is not very different from previous methods such as chain-of-thought reasoning, especially React and self-critic, where at each turn, new instructions are prompted and can be considered as \"AI user\" in this context. The main difference if whether one language model is employed, or two agents in a self-play setup. I agree that there may be some values in using multiple agents as shown in recent works, but using gpt-3.5 with single-shot prompting as the only baseline is not convincing. More importantly, it is not clear what prompts are used for the single-shot baseline and how the prompts are constructed. Furthermore, despite the explanation on why GPT-4 is used to summarize CAMEL before evaluation, this evaluation setting is not convincing because 1. there is no analysis on how much \"hallucination\" or \"error propagation\" is generated because of using GPT-4. In other words, the summarization may be biased by GPT-4 sampling results rather than from CAMEL itself. 2. GPT-4 is used as the evaluator (this is less an issue).\n2. Many details are missing. For example, where the data and tasks are sampled from to construct the dataset and why they are used for evaluation. I would suggest the authors to specify corresponding analysis instead of \"is available in the Appendix\" when revising the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer Z1bn,\n\nThank you for your careful review and valuable feedback. We appreciate your recognition of the novelty of our proposed framework and your insightful observations.\n\n**Responses to Strengths:** We're pleased to note your agreement on the potential of our inception prompting method and the multi-agent collaboration's challenges.\n\nWeaknesses:\n\n1. **Baseline and evaluation.**\n\n    **Response:** We respectfully disagree that the proposed method is not very different from previous methods CoT reasoning, React, and self-critic. CAMEL proposes a multi-agent framework that is very different from single agents approaches such as React and self-critic. In this paper, we showcase realizing multi-agent collaboration for task-solving. We agree that React and self-critic can be also used for task-solving. However, the main difference is that our multi-agent framework provides a broader way to create agents for task-solving. For instance, these agents can use different LLM models and tools. They can have different memory or context windows which is not possible to model with single-agent methods. Moreover, our method can be easily extended to other use cases beyond collaboration such as negotiation, competition, and so on. For games with incomplete information, it is necessary to model using a multi-agent framework.\n  \n    We understand your concern regarding the choice of baselines and the evaluation method. We agree that a comparison with methods such as CoT, React, and self-critic. In our revision, we aim to add these additional baselines. Here, we present additional comparisons only with Zero-CoT in the table below. \n\n    |                        | **Draw** | **Zero-CoT Wins** | **CAMEL Agent Wins** |\n    |------------------------|----------|-------------------|----------------------|\n    | **GPT4 Evaluation**    | 4%       | 28%               | **68%**                  |\n\n\n    For the single-shot baseline, we used the specified task instruction and default system prompt as the prompts. We acknowledge that this might have been unclear in the paper and will make sure to clarify it in the revision.\n\n    Regarding the use of GPT-4, we understand why the points were brought up. For the GPT-4 summarization, the agent is asked to extract full and complete solutions by looking at the conversation between a user and an assistant with particular specializations. The final solution should be purely based on the conversation. Since our solution generation uses gpt-3.5-turbo which had a 4K token limit (at the time), we had no option but to use GPT-4, since it has a larger token limit (8K), to extract the conversational solutions generated by the CAMEL agents to summarized ones, which aims at making the judge (GPT-4 or human) unable to tell the differences between the solutions by identifying the format of the solution. But we agree that this might not be ideal and will consider alternatives. We will include an analysis of this in the revision.\n\n**2. Many details are missing.**\n\n  **Response:** We apologize for any lack of detail in the main text. We make a further clarification here:\n\n  **Figure 3: Data Generation Prompts** is the prompt for generating roles and tasks for the AI society dataset.\n\n  **D Data Generation Prompts for Code** is the prompts for generating languages, domains, and tasks for the Code dataset.\n\n  **Appendix E Meta Data** is the metadata generated by LLMs for AI Society and Code datasets. 50 assistant roles and 50 user roles are generated for AI Society. 20 programming languages and 50 domains are generated for Code.\n\n  **F Math and Science Datasets Generation Details**: The Math dataset consists of 50K problem-solution pairs. The Science dataset consists of 20K Physics problem-solution pairs, 20K Biology problem-solution pairs, and 20K Chemistry problem-solution pairs.\n\n  This information will be incorporated more explicitly in the paper’s body rather than referring to the appendix.\n\n**Questions:**\n\n1. **Inconsistency in naming?**\n\n    **Response:** We would like to clarify that CAMEL refers to our overarching framework, which incorporates several techniques including role-playing, inception prompting, and the generation of multi-turn instructional datasets for fine-tuning. Among these, role-playing is a specific technique we propose within CAMEL to facilitate autonomous collaboration between the AI assistant and AI user. We apologize for any confusion caused by our initial wording and will ensure this distinction is made clear in our revised manuscript.\n\n2. **... Stuck in a loop ...?**\n\n    **Response:** An example is shown in **Appendix G Challenges - Figure 8: Challenges in Role-Playing - Infinite Conversation**:\n\n    > Assistant: \\\n    > It seems like you have already received the necessary information to develop the system. Is there anything else you need assistance with? \\\n    > \\\n    > User: \\\n    > No, thank you for your help. ... \\\n    > \\\n    > ... \\\n    > Assistant: \\\n    > Goodbye! \\\n    > \\\n    > User: \\\n    > Goodbye! \\\n    > ... \\\n    > Assistant: \\\n    > It seems like we are **stuck in a loop**. Is there anything else I can help? \\\n    > \\\n    > User: \\\n    > No, thank you. Have a great day! \\\n    > ...\n\n    The assistant and the user indeed can detect they're stuck in a loop when their responses become repetitive. However, knowing that they are stuck in the loop does not give them access to terminate the program since they are still in their role-play conversations. But this is important for designing the termination conditions. We automatically terminate the loop by checking the frequency of a list of words like termination token *\"<CAMEL_TASK_Done>\"*, *“thank you”* and *“you’re welcome”*, etc. We will make sure to articulate this aspect more clearly in our revised manuscript.\n\n3. **Clarify the questions raised above?**\n\n    **Response:** We hope our responses have clarified the issues raised.\n\nOnce again, thank you for your constructive feedback."}]

Author rebuttal:
To all esteemed reviewers of our NeurIPS 2023 submission,

We express our sincere gratitude for your thorough and insightful reviews of our manuscript. The detailed feedback from each of you provides us with clear guidance on how to refine and improve our work.

**Reviewer wYFm:**

Thank you for recognizing the novelty and intuitiveness of the proposed inception prompting method. Your positive feedback on the clarity of the paper and the potential of supplementary materials for future works is greatly appreciated. We acknowledge the concerns raised about the role/task alignment and the confusion surrounding Fig. 2. Your keen observations about long-distance memory issues with LLM-based agents are especially valuable and will be addressed in our revisions. We attach a **.pdf** file of a two-staged example that addresses the role/task alignment weakness based on your recommendation. The first stage is between a stock trader and a tech lead, and the second stage is between a tech lead and a Python programmer.

> Stage 1: Tech Lead (AI Assistant) v.s. Stock Trader (AI User) \
> Task: Figure out an implementation plan for developing a trading bot for the stock market.


> Stage 2: Python programmer (AI Assistant) v.s. Tech Lead (AI User) \
> Task: Develop a trading bot for the stock market. {Plan obtained from Stage 1}.


We also added an example to show the emergence of knowledge of AI Society-related concepts of a FlanT5 Model. Fine-tuning a FlanT5 on our AI Society data improves the performance of the model on Society related tasks. Additionally, we compare FlanT5 fine-tuned on AI Society data with a LLaMA-7B fine-tuned on the same data and find that they achieve very similar scores with FlanT5 performing slightly better.

| **Dataset**     | **Model 1**                | **Model 2**                     | **Draw** | **Model 1 Wins** | **Model 2 Wins** |
|---|---|---|---|---|---|
| **AI Society**  | FlanT5                     | FlanT5 (+AI Society)            | 1       | 0                | **19**               |
| **AI Society**  | FlanT5 (+AI Society)       | LLaMA-7B (+AI Society)          | 2       | **10**               | 8                |

**Reviewer 6oPa:**

Your feedback regarding the originality of our approach and its potential significance in practical applications is very encouraging. We appreciate your recognition of the clarity of our paper and the potential replicability of the methods. We are attentive to your concerns about the term "large scale language model society" and the need for clarity in distinguishing our approach from hierarchical decision-making. We agree that the mentioned reference [1] and our proposed method have some similarities in terms of reducing the difficulty of reasoning complex tasks. Their connection and discussion will be added to our revisions.

[1] Hierarchical Prompting Assists Large Language Model on Web Navigation

**Reviewer Z1bn:**

We are grateful for your acknowledgment of the innovative nature of our framework and its potential value to the research community. Your observations on the challenges of multi-agent collaboration and the potential issues in our evaluation methods are well-taken. We will certainly delve deeper into the points raised about baselines, evaluation, and the missing details in our paper.

We understand your concern regarding the choice of baselines and the evaluation method. We agree that a comparison with methods such as CoT, React, and self-critic. In our revision, we aim to add these additional baselines. Here, we present additional comparisons only with Zero-CoT in the table below:

|                        | **Draw** | **Zero-CoT Wins** | **CAMEL Agent Wins** |
|------------------------|----------|-------------------|----------------------|
| **GPT4 Evaluation**    | 4%       | 28%               |  **68%**                  |

In conclusion, we are truly thankful for the time and effort each reviewer dedicated to evaluating our submission. Your feedback is instrumental in guiding us toward improving the quality and impact of our research. We look forward to addressing all raised concerns in our revised manuscript.

Warm regards,

NeurIPS 2023 Conference Submission3376 Authors

Meta-review: This work proposes to accomplish tasks autonomously with LLMs but asking them to role play necessary entities for planning and execution. Ideally, one imagines that a human would interact via dialogue with a system to guide the process, but here, the LLM "acts" the part. The results are successful in the domains evaluated and the approach outperforms appropriate baselines.  The key things I would re-raise from the reviews are: 1. Clarifying early that _two_ agents are interacting and we have limited evidence for the strength of a "society" and 2. Evaluation here is based on GPT-4, which while it aligns with humans in the study, is not a guarantee of success in true execution.
Decision: Accept (poster)