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结构化交流,分层行动:LLM多智能体系统的协作框架

Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems

February 16, 2025
作者: Zhao Wang, Sota Moriyama, Wei-Yao Wang, Briti Gangopadhyay, Shingo Takamatsu
cs.AI

摘要

最近基于LLM的多智能体(LLM-MA)系统取得了一些进展,展现出潜力,但在智能体协作处理复杂任务时,仍然存在重大挑战,特别是在管理沟通和改进方面。本文提出了“结构化对话,分层行动”(TalkHier)的新框架,引入了结构化通信协议以进行富有上下文的交流,并采用分层改进系统来解决输出错误、虚假和偏见等问题。TalkHier在各种任务上超越了各种类型的最先进技术,包括推理扩展模型(OpenAI-o1)、开源多智能体模型(例如AgentVerse)以及当前LLM和单智能体基线(例如ReAct、GPT4o)上的多数投票策略,包括开放领域问答、特定领域选择性提问和实用广告文本生成。这些结果突显了其为LLM-MA系统设立新标准的潜力,为更有效、适应性强和协作性更强的多智能体框架铺平了道路。代码可在https://github.com/sony/talkhier找到。
English
Recent advancements in LLM-based multi-agent (LLM-MA) systems have shown promise, yet significant challenges remain in managing communication and refinement when agents collaborate on complex tasks. In this paper, we propose Talk Structurally, Act Hierarchically (TalkHier), a novel framework that introduces a structured communication protocol for context-rich exchanges and a hierarchical refinement system to address issues such as incorrect outputs, falsehoods, and biases. TalkHier surpasses various types of SoTA, including inference scaling model (OpenAI-o1), open-source multi-agent models (e.g., AgentVerse), and majority voting strategies on current LLM and single-agent baselines (e.g., ReAct, GPT4o), across diverse tasks, including open-domain question answering, domain-specific selective questioning, and practical advertisement text generation. These results highlight its potential to set a new standard for LLM-MA systems, paving the way for more effective, adaptable, and collaborative multi-agent frameworks. The code is available https://github.com/sony/talkhier.

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