让大模型通过语言协作，实现多智能体高效决策。
Grounding Natural Language for Multi-agent Decision-Making with Multi-agentic LLMs
- 用提示工程与记忆架构增强语言模型的协作能力。
- 在经典博弈场景中验证了多智能体策略的有效性。
- 适合研究智能体协同与社会困境解决的学者。
语言是推理与协作的基础工具，从日常交流到复杂问题解决均不可或缺。建立共同语言能显著提升智能体间的沟通与理解，促进协调与策略实施。本文通过将大语言模型（LLMs）与多智能体决策算法结合，提出一套系统化的多智能体大语言模型设计框架，涵盖先进提示工程、有效记忆架构、多模态信息处理及通过微调对齐策略。我们在包含显著社会困境和博弈论考量的经典游戏设置中，通过大量消融实验评估了各项设计选择的效果。
原文摘要 · Abstract (English)
Language is a ubiquitous tool that is foundational to reasoning and collaboration, ranging from everyday interactions to sophisticated problem-solving tasks. The establishment of a common language can serve as a powerful asset in ensuring clear communication and understanding amongst agents, facilitating desired coordination and strategies. In this work, we extend the capabilities of large language models (LLMs) by integrating them with advancements in multi-agent decision-making algorithms. We propose a systematic framework for the design of multi-agentic large language models (LLMs), focusing on key integration practices. These include advanced prompt engineering techniques, the development of effective memory architectures, multi-modal information processing, and alignment strategies through fine-tuning algorithms. We evaluate these design choices through extensive ablation studies on classic game settings with significant underlying social dilemmas and game-theoretic considerations.
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