标题： 重新思考所有证据：通过冲突驱动的摘要增强可信的检索增强生成 显示英文标题

标题： Rethinking All Evidence: Enhancing Trustworthy Retrieval-Augmented Generation via Conflict-Driven Summarization

摘要： 检索增强生成（RAG）通过将参数知识与外部检索内容相结合，增强了大型语言模型（LLMs）的能力。 然而，由于内部不一致或噪声检索内容导致的知识冲突会严重削弱RAG系统的生成可靠性。在本工作中，我们认为在生成响应之前，LLMs应重新考虑所有证据，包括检索内容和内部知识。我们提出了CARE-RAG（面向冲突的RAG可靠证据），一种通过所有可用证据的冲突驱动摘要来提高可信度的新框架。CARE-RAG首先通过比较参数记录来推导出参数感知的证据，以识别多样化的内部视角。 然后，它对检索到的证据进行精炼，以生成上下文感知的证据，去除无关或具有误导性的内容。为了检测和摘要冲突，我们蒸馏了一个3B的LLaMA3.2模型来进行冲突驱动的摘要，从而实现在多个来源之间的可靠综合。为了进一步确保评估的完整性，我们引入了一个QA修复步骤来纠正过时或模糊的基准答案。在包含检索数据的修订后的QA数据集上的实验表明，CARE-RAG始终优于强大的RAG基线，特别是在存在噪声或冲突证据的场景中。 显示英文摘要

摘要： 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synthesis across multiple sources.To further ensure evaluation integrity, we introduce a QA Repair step to correct outdated or ambiguous benchmark answers.Experiments on revised QA datasets with retrieval data show that CARE-RAG consistently outperforms strong RAG baselines,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.