用大模型优化检索，让搜索结果更准更全。
ReBOL: Retrieval via Bayesian Optimization with Batched LLM Relevance Observations and Query Reformulation
- 用大模型改写查询并构建文档相关性概率模型
- 批量评估文档相关性，持续优化检索结果
- 在多个数据集上提升召回率，适合需要高精度搜索的场景
大模型重排序受限于向量相似度召回的前k个文档，无法实现查询与文档的上下文交互，也难以捕捉多模态相关性分布。尽管大模型查询改写试图通过生成新查询提升召回率，但后续仍依赖向量相似度检索。为此，我们提出ReBOL，首先利用大模型查询改写初始化文档相关性的多模态贝叶斯优化后验，然后迭代获取文档批次进行大模型评分，并更新后验以优化相关性。在五个BEIR数据集上，使用Gemini-2.5-Flash-Lite和GPT-5.2两个大模型评估，ReBOL在所有测试中均取得更高召回率，例如在Robust04数据集上，recall@100达到46.5%（对比最优基线35.0%），NDCG@10为63.6%（对比61.2%），同时保持与大模型重排序相当的延迟。
原文摘要 · Abstract (English)
LLM-reranking is limited by the top-k documents retrieved by vector similarity, which neither enables contextual query-document token interactions nor captures multimodal relevance distributions. While LLM query reformulation attempts to improve recall by generating improved or additional queries, it is still followed by vector similarity retrieval. We thus propose to address these top-k retrieval stage failures by introducing ReBOL, which 1) uses LLM query reformulations to initialize a multimodal Bayesian Optimization (BO) posterior over document relevance, and 2) iteratively acquires document batches for LLM query-document relevance scoring followed by posterior updates to optimize relevance. After exploring query reformulation and document batch diversification techniques, we evaluate ReBOL against LLM reranker baselines on five BEIR datasets and using two LLMs (Gemini-2.5-Flash-Lite, GPT-5.2). ReBOL consistently achieves higher recall and competitive rankings, for example compared to the best LLM reranker on the Robust04 dataset with 46.5% vs. 35.0% recall@100 and 63.6% vs. 61.2% NDCG@10. We also show that ReBOL can achieve comparable latency to LLM rerankers.
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