对话的几何学：基于图谱的语言模型揭示多智能体协作的协同团队
The Geometry of Dialogue: Graphing Language Models to Reveal Synergistic Teams for Multi-Agent Collaboration
AI总结：
本文提出一种基于交互的自动团队组建框架，通过构建语言模型图谱揭示多智能体协作的协同团队，实验表明其能发现功能一致的团队并优于随机基线。
摘要：
尽管基于大语言模型的多智能体方法在超越单个模型能力方面具有前景，但其成功高度依赖于协同团队的组成。然而，形成最优团队是一个重大挑战，因为大多数模型的内在不透明性阻碍了有效协作所需的内部特征。本文提出了一种交互中心的自动团队组建框架，无需任何先验知识，包括其内部架构、训练数据或任务表现。我们的方法构建了一个“语言模型图”，通过成对对话的语义一致性映射模型间的关系，并应用社区检测来识别协同模型集群。在多样化的LLM上的实验表明，所提出的方法发现了功能一致的团队，反映了其潜在的专业化。通过特定主题的预训练对话，协同团队在下游基准上优于随机基线，并在已知模型专业化基础上达到可比的准确性。我们的发现为自动设计协作多智能体LLM团队提供了新的基础。
英文摘要：
While a multi-agent approach based on large language models (LLMs) represents a promising strategy to surpass the capabilities of single models, its success is critically dependent on synergistic team composition. However, forming optimal teams is a significant challenge, as the inherent opacity of most models obscures the internal characteristics necessary for effective collaboration. In this paper, we propose an interaction-centric framework for automatic team composition that does not require any prior knowledge including their internal architectures, training data, or task performances. Our method constructs a "language model graph" that maps relationships between models from the semantic coherence of pairwise conversations, and then applies community detection to identify synergistic model clusters. Our experiments with diverse LLMs demonstrate that the proposed method discovers functionally coherent groups that reflect their latent specializations. Priming conversations with specific topics identified synergistic teams which outperform random baselines on downstream benchmarks and achieve comparable accuracy to that of manually-curated teams based on known model specializations. Our findings provide a new basis for the automated design of collaborative multi-agent LLM teams.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
