EviQE: Evidence Selection for LLM-Based Query Expansion

2026-09-13

AI Summary

为解决LLM查询扩展中证据选择问题，提出EviQE方法，通过聚合多个重述器检索的文档并选择紧凑证据集来优化查询扩展。

Abstract

LLM-based query expansion increasingly conditions reformulation on documents retrieved from the target corpus, yet most work focuses on how to generate expansions rather than which documents the model should read. We propose EviQE, which aggregates documents retrieved by multiple reformulators, selects a compact evidence set, and uses it for one grounded expansion step. This separates evidence selection from generation and treats reformulators as complementary retrieval perspectives. Across three TREC DL and five BEIR benchmarks, reformulators frequently retrieve distinct relevant documents, so pooled candidates provide higher relevant-document coverage than any individual source. The strongest gains come from relevance-based evidence selection: LLM-Score consistently outperforms direct reformulation, cold-start expansion, and single-source seeded expansion. Additional retrieval-generation rounds provide little benefit once strong conditioning evidence has been selected and can reduce effectiveness.

Problem

• LLM-based query expansion
• evidence selection
• reformulation

Innovation

• EviQE
• query expansion
• evidence selection
• reformulators
• relevance-based