Assessing the Downstream Utility of Evidence-Aware Retrieval in RAG

2026-08-26

AI Summary

研究评估了证据感知检索在RAG中的下游效用，通过分析其在不同角色下的表现，发现该方法虽改变检索排名，但对提升生成质量的可靠性有限。

Abstract

Retrieval evaluation for retrieval-augmented generation (RAG) is increasingly designed around whether retrieved passages contain evidence that can support generation, rather than topical relevance alone. We study whether this closer alignment with downstream evidence needs also makes retrieval evaluation more useful for the decisions built from it. Across five retrieval benchmarks and an end-to-end TREC RAG 2025 setting, we examine an answer-support signal in four roles: comparing retrievers, guiding retrieval training and system selection, predicting downstream answer quality, and filtering the evidence supplied to a generator. The signal changes retrieval rankings, but its downstream value is not uniform. It does not reliably improve retriever training; the benefit of using it for system selection depends on how the generator is instructed to use the retrieved evidence; and retrieval scores based on it do not robustly predict answer quality on unseen topics. In a direct evidence intervention, human annotators confirm that filtering preferentially preserves passages containing useful answer evidence, yet different answer evaluators reach different conclusions about whether the resulting answers improve. These results show that making retrieval evaluation more closely reflect the evidence needed for generation does not by itself make every downstream use of that evaluation more reliable. RAG evaluation methods should therefore be assessed with respect to the particular comparisons, decisions, and conclusions they are intended to support.

Problem

• Retrieval-Augmented Generation
• Evidence-Aware Retrieval
• Downstream Utility
• Retrieval Evaluation
• Answer Quality

Innovation

• evidence-aware retrieval
• retrieval-augmented generation (RAG)
• downstream utility
• answer-support signal
• retrieval evaluation