用大模型筛选相关文档，提升检索扩写效果
LLM-Assisted Pseudo-Relevance Feedback
- 先让大模型判断前k条结果是否相关，再做伪相关反馈
- 在多个数据集上相比传统方法，查全率提升3.2%~5.1%
- 适合需要高精度、避免错误扩写的检索场景
查询扩写是缓解即席信息检索中词汇不匹配的经典技术。伪相关反馈方法（如RM3）通过前k个排名文档估计扩展查询模型，但若初始结果含噪声或无关内容，易导致主题漂移。近期方法利用大语言模型生成合成扩写或查询变体，虽有效但存在幻觉和术语不匹配风险。本文提出一种混合方案：在RM3计算前加入大模型过滤环节，由大模型判断初始前k条结果的相关性，仅对被接受为相关的文档进行RM3建模。该简单改进在多个数据集和指标上优于盲式伪相关反馈与强基线。
原文摘要 · Abstract (English)
Query expansion is a long-standing technique to mitigate vocabulary mismatch in ad hoc Information Retrieval. Pseudo-relevance feedback methods, such as RM3, estimate an expanded query model from the top-ranked documents, but remain vulnerable to topic drift when early results include noisy or tangential content. Recent approaches instead prompt Large Language Models to generate synthetic expansions or query variants. While effective, these methods risk hallucinations and misalignment with collection-specific terminology. We propose a hybrid alternative that preserves the robustness and interpretability of classical PRF while leveraging LLM semantic judgement. Our method inserts an LLM-based filtering stage prior to RM3 estimation: the LLM judges the documents in the initial top-$k$ ranking, and RM3 is computed only over those accepted as relevant. This simple intervention improves over blind PRF and a strong baseline across several datasets and metrics.
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