RAG-E：用局部解释量化 RAG 中检索器与生成器的对齐程度

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Abstract:Retrieval-Augmented Generation (RAG) systems combine dense retrievers and language models to ground outputs in external documents. However, the interaction between these components remains opaque, creating challenges for deployment in high-stakes domains. We present RAG-E, an end-to-end explainability framework that quantifies retriever-generator alignment through mathematically grounded attribution methods. Our approach adapts Integrated Gradients for retriever analysis, proposes a Monte Carlo-stabilized Shapley Value approximation for generator attribution, and introduces the Weighted Alignment between Retriever and Generator (WARG) metric to measure how closely the generator's document usage aligns with retriever rankings. Experiments on PopQA, QAMPARI, and TREC CAST datasets reveal substantial misalignment: depending on the model and setting, generators often ignore top-ranked documents and rely on documents ranked as less relevant. We show that WARG captures retriever-generator alignment better than Pearson and Spearman correlations and can serve as an indicator of RAG performance. RAG-E and WARG provide a practical framework for auditing this interaction, enabling more reliable and transparent RAG systems.

Submission history

From: Korbinian Randl [view email]
[v1] Thu, 29 Jan 2026 14:47:00 UTC (1,954 KB)
[v2] Tue, 7 Jul 2026 09:51:50 UTC (979 KB)
[v3] Wed, 7 Oct 2026 19:36:30 UTC (1,145 KB)