Evidence Interfaces Shape How Retrieval-Augmented Readers Use Support

2026-07-19

AI Summary

This work addresses a key challenge in multi-hop retrieval-augmented generation (RAG): disentangling whether performance bottlenecks stem from missing supporting evidence or the reader’s inability to effectively utilize available evidence. To this end, the authors introduce the concept of an “evidence interface” and, for the first time, systematically decouple the impact of missing support chains from evidence interface effects by evaluating adapter-trained readers across three multi-hop QA benchmarks with annotated support chains. Using original contexts, retrieved passages, and oracle-supported inputs, they demonstrate that a support-supervised ranker significantly improves support coverage on 2Wiki and MuSiQue, recovers original-context performance at substantially lower prompting costs, and approaches the upper bound set by gold support annotations.

Abstract

In multi-hop RAG evaluation, a top-k answer score can hide two different failures: the retrieval window may drop part of the support chain, or it may contain support in a form the adapted reader does not use well. We call this reader-facing form of retrieved evidence an evidence interface. Using three support-annotated multi-hop QA benchmarks, we compare matched adapted readers trained with raw context, retrieval windows, and gold-support diagnostic renderings. These comparisons distinguish support-availability failures from remaining reader-interface effects. Top-k windows become interpretable only after checking whether the complete annotated support chain survives: when it does, short ranked windows can match or improve over raw context; when it does not, missing support explains much of the loss. Gold support-first improves matched readers; on 2Wiki and MuSiQue, a support-supervised ranker raises coverage and recovers raw-context quality at lower prompt cost, while retaining gold headroom. Support-removal checks further show that the gains rely on exposed evidence, not only answer priors. On support-annotated evaluations, top-k answer scores should therefore be reported together with complete-support coverage.

Problem

• retrieval-augmented generation
• evidence interface
• multi-hop QA
• support chain
• reader adaptation

Innovation

• evidence interface
• retrieval-augmented generation
• multi-hop QA
• support chain
• reader adaptation