CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2303.17760
Summary
Chat-based LLMs handle complex tasks well, but a person usually has to steer each step with careful prompts, which takes expertise and time. The authors from KAUST propose role-playing, a framework in which two LLM agents work through a task together with little human involvement. A human supplies only a rough idea and two roles. A task-specifier agent then turns the idea into a concrete task. An AI user issues step-by-step instructions, and an AI assistant answers with solutions. Inception prompting fixes each agent's role at the start to prevent typical failures: agents swapping roles, the assistant echoing instructions, empty replies and endless message loops. The recorded dialogues provide large instruction-following datasets. Solutions produced through role-playing were preferred over single-shot gpt-3.5-turbo outputs by both GPT4 and human judges. Fine-tuning LLaMA on progressively larger portions of the generated data showed new abilities emerging. The work offers a scalable way to create conversational training data and study multi-agent cooperation, with the code released openly.
Key points
- The framework uses three agents (task specifier, AI user, AI assistant) guided by inception prompting. The running example has a Stock Trader and a Python Programmer building a stock-trading bot.
- Released datasets:
- AI Society and Code: multi-turn, task-oriented conversations.
- Math and Science: single-turn question-answer sets.
- Misalignment: simulated malicious uses showing the risks of unaligned autonomous agents.
- Role-playing outputs beat single-shot gpt-3.5-turbo in both GPT4-based and human evaluations.
- LLaMA fine-tuned on the generated data was benchmarked for code generation on HumanEval and HumanEval+.
- The library, with agents, prompts, data pipelines and analysis tools, is open source at github.com/camel-ai/camel (NeurIPS 2023, arXiv:2303.17760).
Cited by
- Qwen Technical Report
- ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
- AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
- Large Language Model based Multi-Agents: A Survey of Progress and Challenges
- Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
- What matters when building vision-language models?
- Agent Laboratory: Using LLM Agents as Research Assistants
- Aligning Large Language Models with Human: A Survey
- RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
- Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration
- OpenHands: An Open Platform for AI Software Developers as Generalist Agents
- The Rise and Potential of Large Language Model Based Agents: A Survey
- WizardLM: Empowering large pre-trained language models to follow complex instructions
- Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
- MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
- A Survey of Large Language Models
- SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents
Cite
@misc{li2023camel,
title = {{CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society}},
author = {Li, Guohao and Hammoud, Hasan Abed Al Kader and Itani, Hani and Khizbullin, Dmitrii and Ghanem, Bernard},
year = {2023},
doi = {10.48550/arxiv.2303.17760},
eprint = {2303.17760},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2303.17760}
}References
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