让大模型更精准地从检索文档中挖掘关键线索，提升推理可信度。
ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generation
- 基于检索内容提取关键线索，生成多条推理路径
- 通过奖励机制优化选择最优推理路径，显著提升推理完整性
- 在噪声数据或无明确线索时仍能有效识别支持证据
检索增强生成（RAG）通过引入外部知识提升大模型的准确性，但现有系统常未能充分挖掘检索到的文档，难以提取并整合支撑推理的关键线索，尤其当证据隐含、分散或被噪声掩盖时。为此，我们提出ClueAnchor框架，通过线索锚定的推理探索与优化机制，从检索内容中提取关键线索，并基于不同知识配置生成多条推理路径，利用基于奖励的偏好优化策略，在给定上下文中选择最合适的路径。实验表明，ClueAnchor在推理完整性和鲁棒性上显著优于现有RAG基线。进一步分析显示，该方法对噪声或部分相关检索内容具有强抗干扰能力，且在推理阶段无需显式线索监督即可识别支持证据。代码已开源：https://github.com/thunlp/ClueAnchor。
原文摘要 · Abstract (English)
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.
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