让检索与生成动态互动，提升复杂问答的准确率
Test-time Corpus Feedback: From Retrieval to RAG
- 引入反馈机制使检索过程可迭代优化
- 通过上下文反馈提升检索相关性与答案精度
- 适合研究RAG系统优化的研究者和工程师
检索增强生成（RAG）已成为知识密集型自然语言处理任务的标准框架，结合大语言模型（LLMs）与外部语料库的文档检索。尽管广泛应用，多数RAG流程仍把检索与推理视为独立步骤，仅一次性检索文档后生成答案，缺乏后续交互。这种静态设计常限制在需要迭代证据收集或高精度检索的复杂任务上的表现。近期信息检索（IR）与自然语言处理（NLP）领域开始通过引入自适应检索与排序方法来弥补这一差距，这些方法整合了反馈信号。本文综述了先进的检索与排序机制，其核心是融入反馈信号。我们根据反馈信号的来源与作用，将其分类为改进查询、检索上下文或文档池的类型。通过整合这些进展，旨在弥合信息检索与自然语言处理的视角差异，并强调检索作为端到端RAG系统中可学习的动态组件。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) has emerged as a standard framework for knowledge-intensive NLP tasks, combining large language models (LLMs) with document retrieval from external corpora. Despite its widespread use, most RAG pipelines continue to treat retrieval and reasoning as isolated components, retrieving documents once and then generating answers without further interaction. This static design often limits performance on complex tasks that require iterative evidence gathering or high-precision retrieval. Recent work in both the information retrieval (IR) and NLP communities has begun to close this gap by introducing adaptive retrieval and ranking methods that incorporate feedback. In this survey, we present a structured overview of advanced retrieval and ranking mechanisms that integrate such feedback. We categorize feedback signals based on their source and role in improving the query, retrieved context, or document pool. By consolidating these developments, we aim to bridge IR and NLP perspectives and highlight retrieval as a dynamic, learnable component of end-to-end RAG systems.
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