arXiv 2506.039392025-06-04cs.AIcs.CL

Graph Counselor：通过多智能体协同的自适应图探索增强大语言模型推理

Graph Counselor: Adaptive Graph Exploration via Multi-Agent Synergy to Enhance LLM Reasoning

Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；School of Mathematics, Harbin Institute of Technology（哈尔滨工业大学数学学院）；Department of Control Science and Engineering, Harbin Institute of Technology（哈尔滨工业大学控制科学与工程学院）

Junqi Gao, Xiang Zou, YIng Ai, Dong Li, Yichen Niu, Biqing Qi, Jianxing Liu

备注：: Accepted by ACL 2025

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AI总结：

针对现有GraphRAG信息聚合低效和推理机制僵化问题，提出基于多智能体协同的Graph Counselor方法，通过自适应图信息提取模块和多视角自反思机制，在多个图推理任务中取得更高准确性和泛化能力。

摘要：

图检索增强生成（GraphRAG）通过显式建模知识关系，有效增强了外部知识整合能力，从而提升了大型语言模型（LLMs）在专业领域中的事实准确性和生成质量。然而，现有方法存在两个固有局限：1）信息聚合效率低下：它们依赖单一智能体和固定的迭代模式，难以自适应地捕获图数据中的多层次文本、结构和度数信息。2）推理机制僵化：它们采用预设的推理方案，无法动态调整推理深度，也难以实现精确的语义修正。为克服这些局限，我们提出了Graph Counselor，一种基于多智能体协作的GraphRAG方法。该方法使用自适应图信息提取模块（AGIEM），其中规划智能体、思考智能体和执行智能体协同工作，以精确建模复杂图结构并动态调整信息提取策略，从而应对多层次依赖建模和自适应推理深度的挑战。此外，多视角自反思（SR）模块通过自反思和反向推理机制，提高了推理结果的准确性和语义一致性。实验表明，Graph Counselor在多个图推理任务中优于现有方法，展现出更高的推理准确性和泛化能力。我们的代码可在 https://github.com/gjq100/Graph-Counselor.git 获取。

英文摘要：

Graph Retrieval Augmented Generation (GraphRAG) effectively enhances external knowledge integration capabilities by explicitly modeling knowledge relationships, thereby improving the factual accuracy and generation quality of Large Language Models (LLMs) in specialized domains. However, existing methods suffer from two inherent limitations: 1) Inefficient Information Aggregation: They rely on a single agent and fixed iterative patterns, making it difficult to adaptively capture multi-level textual, structural, and degree information within graph data. 2) Rigid Reasoning Mechanism: They employ preset reasoning schemes, which cannot dynamically adjust reasoning depth nor achieve precise semantic correction. To overcome these limitations, we propose Graph Counselor, an GraphRAG method based on multi-agent collaboration. This method uses the Adaptive Graph Information Extraction Module (AGIEM), where Planning, Thought, and Execution Agents work together to precisely model complex graph structures and dynamically adjust information extraction strategies, addressing the challenges of multi-level dependency modeling and adaptive reasoning depth. Additionally, the Self-Reflection with Multiple Perspectives (SR) module improves the accuracy and semantic consistency of reasoning results through self-reflection and backward reasoning mechanisms. Experiments demonstrate that Graph Counselor outperforms existing methods in multiple graph reasoning tasks, exhibiting higher reasoning accuracy and generalization ability. Our code is available at https://github.com/gjq100/Graph-Counselor.git.

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。