Beyond One-Shot Expansion: Contrastive Evidence Exploration for Multi-Hop Retrieval

2026-09-07

AI Summary

本文针对多跳问答中证据检索难题，提出一种无需训练的检索框架，通过探索证据条件、对比细化和覆盖率感知排名来改进检索质量。

Abstract

Retrieval-augmented generation (RAG) critically depends on retrieving the evidence necessary for effective reasoning. However, this remains particularly challenging in multi-hop question answering (QA), where supporting passages are often linked through intermediate entities and relations that must be progressively uncovered. Existing retrieval approaches typically rely on a single retrieval intent or one-shot query expansion, limiting their ability to adapt to newly retrieved evidence and potentially introducing noisy or redundant retrieval signals. To address these limitations, we propose a training-free multi-hop retrieval framework that integrates evidence-conditioned exploration, passage-specific contrastive refinement, and coverage-aware final ranking. During offline indexing, the framework constructs passage-specific contrastive facets that characterize each passage relative to its semantically similar neighbors, providing fine-grained signals to distinguish closely related candidates. At inference time, the framework iteratively retrieves evidence, generates probes targeting unresolved information needs, refines candidate relevance using the contrastive facets, and selects a complementary set of passages that collectively cover diverse evidence-seeking intents. Experiments on MuSiQue, HotpotQA, and 2WikiMultihopQA demonstrate consistent improvements in retrieval quality and downstream QA performance over baselines.

Problem

• multi-hop question answering
• retrieval-augmented generation
• evidence retrieval

Innovation

• evidence-conditioned exploration
• passage-specific contrastive refinement
• coverage-aware final ranking