ClueAnchor：面向检索增强生成的线索锚定知识推理探索与优化
ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generation
AI总结：
针对现有RAG系统对检索文档利用不足、难提取关键推理线索的问题，提出ClueAnchor框架，通过线索提取、多路径生成及奖励偏好优化增强推理，在完整性、鲁棒性及抗噪性上均优于基线。
摘要：
检索增强生成（RAG）通过外部知识增强大语言模型（LLM）以提升事实准确性。然而，现有RAG系统往往对检索到的文档利用不足，无法提取并整合支撑忠实、可解释推理所需的关键线索，在相关证据隐含、分散或被噪声掩盖的场景中尤为明显。为解决这一问题，我们提出ClueAnchor——一种通过线索锚定推理探索与优化增强RAG的新型框架。ClueAnchor从检索内容中提取关键线索，基于不同知识配置生成多条推理路径，通过基于奖励的偏好优化为给定上下文选择最合适的推理路径，以此优化模型。实验表明，ClueAnchor在推理的完整性和鲁棒性上显著优于以往的RAG基线。进一步分析证实，该框架对含噪声或部分相关的检索内容具备强韧性，且在推理过程中无显式线索监督的情况下仍能识别支撑证据。所有代码可在https://github.com/thunlp/ClueAnchor获取。
英文摘要：
Retrieval-Augmented Generation (RAG) augments Large Language Models (LLMs) with external knowledge to improve factuality. However, existing RAG systems frequently underutilize the retrieved documents, failing to extract and integrate the key clues needed to support faithful and interpretable reasoning, especially in cases where relevant evidence is implicit, scattered, or obscured by noise. To address this issue, we propose ClueAnchor, a novel framework for enhancing RAG via clue-anchored reasoning exploration and optimization. ClueAnchor extracts key clues from retrieved content and generates multiple reasoning paths based on different knowledge configurations, optimizing the model by selecting the most appropriate reasoning path for the given context through reward-based preference optimization. Experiments show that ClueAnchor significantly outperforms prior RAG baselines in the completeness and robustness of reasoning. Further analysis confirms its strong resilience to noisy or partially relevant retrieved content, as well as its capability to identify supporting evidence even in the absence of explicit clue supervision during inference. All codes are available at https://github.com/thunlp/ClueAnchor.
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。
