用大模型提取推理依据，让检索结果更匹配生成需求。
Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation
- 用大模型自动提取回答问题所需的推理依据
- 基于推理依据重新排序文档，提升相关性
- 适用于需要精准知识支持的问答系统
在检索增强生成（RAG）流程中，重排器与生成器分别负责文档排序和回答生成。由于预训练数据和目标差异，重排器选出的相关文档与生成器实际需要的内容存在差距。为此，我们提出RADIO框架，通过大语言模型（LLM）提取回答问题所需的推理依据，并基于该依据对文档进行重排序，同时微调重排器以对齐偏好。我们在两个任务、三个数据集上进行了大量实验，验证了该方法优于基线。代码已公开，便于复现。
原文摘要 · Abstract (English)
The reranker and generator are two critical components in the Retrieval-Augmented Generation (i.e., RAG) pipeline, responsible for ranking relevant documents and generating responses. However, due to differences in pre-training data and objectives, there is an inevitable gap between the documents ranked as relevant by the reranker and those required by the generator to support answering the query. To address this gap, we propose RADIO, a novel and practical preference alignment framework with RAtionale DIstillatiOn. Specifically, we first propose a rationale extraction method that leverages the reasoning capabilities of Large Language Models (LLMs) to extract the rationales necessary for answering the query. Subsequently, a rationale-based alignment process is designed to rerank the documents based on the extracted rationales, and fine-tune the reranker to align the preferences. We conduct extensive experiments on two tasks across three datasets to demonstrate the effectiveness of our approach compared to baseline methods. Our code is released online to ease reproduction.
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