让大模型学会在噪声中识别有用信息，提升检索增强生成的可靠性。
Tackling the Inherent Difficulty of Noise Filtering in RAG
- 设计新微调方法，增强模型分辨相关与无关信息的能力。
- 在多个基准上显著提升模型对噪声的鲁棒性和性能。
- 适合需要高可信度生成的场景，如医疗、金融问答。
检索增强生成（RAG）通过引入外部知识来提升大语言模型（LLM）的表现并减少幻觉，但检索到的内容常含噪声或无关文档，可能降低性能甚至引发幻觉。尽管已有多种去噪方法，但我们指出从检索结果中识别无关信息本质上困难，且有限数量的Transformer层难以解决此问题，导致检索器无法完全过滤无关文档。因此，大模型必须具备抗噪声能力，但标准微调方法因注意力结构限制，往往无法有效实现对相关信息的选择性利用和对无关内容的忽略。为此，我们提出一种新型微调方法，旨在增强模型在检索文档中区分相关与无关信息的能力。在多个基准上的大量实验表明，该方法显著提升了大模型的鲁棒性和性能。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) has become a widely adopted approach to enhance Large Language Models (LLMs) by incorporating external knowledge and reducing hallucinations. However, noisy or irrelevant documents are often introduced during RAG, potentially degrading performance and even causing hallucinated outputs. While various methods have been proposed to filter out such noise, we argue that identifying irrelevant information from retrieved content is inherently difficult and limited number of transformer layers can hardly solve this. Consequently, retrievers fail to filter out irrelevant documents entirely. Therefore, LLMs must be robust against such noise, but we demonstrate that standard fine-tuning approaches are often ineffective in enabling the model to selectively utilize relevant information while ignoring irrelevant content due to the structural constraints of attention patterns. To address this, we propose a novel fine-tuning method designed to enhance the model's ability to distinguish between relevant and irrelevant information within retrieved documents. Extensive experiments across multiple benchmarks show that our approach significantly improves the robustness and performance of LLMs.
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