让大模型重新审视所有证据，解决信息冲突问题。
Rethinking All Evidence: Enhancing Trustworthy Retrieval-Augmented Generation via Conflict-Driven Summarization
- 对比内部知识与外部检索内容，识别多角度观点
- 通过冲突驱动摘要，提升生成结果可靠性
- 适合需要高可信度问答的场景，如医疗、法律
检索增强生成（RAG）通过融合大语言模型（LLMs）的参数化知识与外部检索内容来增强生成能力。然而，由内部不一致或噪声检索内容引发的知识冲突会严重降低RAG系统的可靠性。本文认为，大模型在生成回应前应重新审视所有证据，包括检索内容和内部知识。为此，我们提出CARE-RAG框架，通过冲突驱动摘要提升可信度。CARE-RAG首先通过比较参数记录生成参数感知证据，以识别内部多元视角；接着对检索证据进行优化，剔除无关或误导性内容，生成上下文感知证据。为检测并总结冲突，我们使用30亿参数的LLaMA3.2模型进行冲突驱动摘要，实现多源信息可靠融合。为确保评估完整性，引入问答修复步骤以纠正过时或模糊的基准答案。在含检索数据的修订问答数据集上的实验表明，CARE-RAG在噪声或冲突场景下持续优于主流RAG基线。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating their parametric knowledge with external retrieved content. However, knowledge conflicts caused by internal inconsistencies or noisy retrieved content can severely undermine the generation reliability of RAG systems.In this work, we argue that LLMs should rethink all evidence, including both retrieved content and internal knowledge, before generating responses.We propose CARE-RAG (Conflict-Aware and Reliable Evidence for RAG), a novel framework that improves trustworthiness through Conflict-Driven Summarization of all available evidence.CARE-RAG first derives parameter-aware evidence by comparing parameter records to identify diverse internal perspectives. It then refines retrieved evidences to produce context-aware evidence, removing irrelevant or misleading content. To detect and summarize conflicts, we distill a 3B LLaMA3.2 model to perform conflict-driven summarization, enabling reliable synthesis across multiple sources.To further ensure evaluation integrity, we introduce a QA Repair step to correct outdated or ambiguous benchmark answers.Experiments on revised QA datasets with retrieval data show that CARE-RAG consistently outperforms strong RAG baselines, especially in scenarios with noisy or conflicting evidence.
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