重新审视RAG检索器：一个信息论基准
Revisiting RAG Retrievers: An Information Theoretic Benchmark
AI总结：
本文提出MIGRASCOPE基准，通过信息论方法分析RAG检索器的性能、冗余和协同效应，揭示最佳检索器组合策略。
摘要：
检索增强生成（RAG）系统依赖于检索模块来为大语言模型提供相关上下文。尽管最近提出了许多检索器，每个都基于不同的排序原则，如词汇匹配、密集嵌入或图引文，但仍然缺乏对这些机制差异和重叠的系统理解。现有的基准主要比较整个RAG管道或引入新数据集，提供很少关于选择或组合检索器本身的指导。那些直接比较检索器的使用有限的评估工具，无法捕捉互补和重叠的优势。本文提出了MIGRASCOPE，一个基于互信息的RAG检索器分析范围。我们重新审视最先进的检索器，并引入基于信息和统计估计理论的原理性度量标准，以量化检索质量、冗余、协同作用和边际贡献。我们进一步表明，如果选择得当，检索器的集合会优于任何单一检索器。我们利用开发的工具在主要RAG语料库上提供对最新检索器贡献水平的独特见解。我们的发现为现代检索技术的结构提供了新的视角，并为设计稳健和高效的RAG系统提供了可操作的指导。
英文摘要：
Retrieval-Augmented Generation (RAG) systems rely critically on the retriever module to surface relevant context for large language models. Although numerous retrievers have recently been proposed, each built on different ranking principles such as lexical matching, dense embeddings, or graph citations, there remains a lack of systematic understanding of how these mechanisms differ and overlap. Existing benchmarks primarily compare entire RAG pipelines or introduce new datasets, providing little guidance on selecting or combining retrievers themselves. Those that do compare retrievers directly use a limited set of evaluation tools which fail to capture complementary and overlapping strengths. This work presents MIGRASCOPE, a Mutual Information based RAG Retriever Analysis Scope. We revisit state-of-the-art retrievers and introduce principled metrics grounded in information and statistical estimation theory to quantify retrieval quality, redundancy, synergy, and marginal contribution. We further show that if chosen carefully, an ensemble of retrievers outperforms any single retriever. We leverage the developed tools over major RAG corpora to provide unique insights on contribution levels of the state-of-the-art retrievers. Our findings provide a fresh perspective on the structure of modern retrieval techniques and actionable guidance for designing robust and efficient RAG systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
