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This study investigates the effectiveness of evidence-aware retrieval evaluation in retrieval-augmented generation (RAG) systems, focusing on its impact on downstream decision-making. By analyzing five retrieval benchmarks and a TREC RAG 2025 setting, the authors find that while evidence alignment alters retrieval rankings, it does not consistently enhance retriever training or reliably predict answer quality across unseen topics. The findings suggest that the utility of evidence-aware evaluation is context-dependent, necessitating careful consideration of its application in RAG systems.
Evidence-aware retrieval evaluation can change rankings but doesn't guarantee improved answer quality or retriever training, revealing a nuanced relationship between evaluation methods and downstream utility.
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.
