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.

翻译：查询扩展是缓解特定信息检索中词汇失配问题的长期技术。伪相关反馈方法（如RM3）通过排名靠前的文档估计扩展查询模型，但当初始结果包含噪声或无关内容时，仍易受主题漂移影响。近期研究转而提示大语言模型生成合成扩展或查询变体。这些方法虽有效，但存在幻觉风险且可能与特定文档集的术语体系失配。我们提出一种混合方案，在保持经典伪相关反馈鲁棒性与可解释性的同时，利用LLM的语义判断能力。该方法在RM3估计前插入基于LLM的过滤阶段：LLM对初始排名前$k$的文档进行相关性判定，仅基于被判定为相关的文档计算RM3。这种简单干预在多个数据集和评价指标上均优于盲目伪相关反馈及强基线方法。