AI 中文总结
研究现代信息检索系统中相关性操作的局限，提出将其理解为近似后验推理，扩展经典概率检索形式，通过显式似然 - 先验分解转移计算，纳入查询独立文档效用，经实验证明能提升检索效果并给出研究方向。
AI 中文摘要
现代信息检索系统通常将相关性作为推理时计算的查询条件分数来操作。这种设计选择已占主导地位，尽管概率检索和大规模搜索中存在文档和查询先验的悠久历史，但很少讨论相关性的替代分解。随着神经排序模型计算成本增加且检索管道扩展，这种查询时评分的整体观点越来越受限。我们认为现代信息访问系统更自然地被理解为执行近似后验推理，通过查询相关似然和查询独立先验的分阶段组合来细化相关性。我们将经典概率检索形式扩展到当代学习系统，展示了显式似然 - 先验分解如何在分离文档级和交互级信念时为将计算转移到离线提供新机会。我们给出实证证据，纳入查询独立文档效用可以补充现有排序器并以最小的查询时计算（仅分数融合）提高有效性。具体而言，学习到的先验通过排序融合改进第一阶段检索（在TREC DL - 2019上nDCG@10提高0.046，在TREC DL - 2020上提高0.029），也改进下游重排，在LLM重排器RankZephyr上收益最大（在TREC DL - 2020上nDCG@10提高0.054）。最后，我们讨论这种分解如何与更广泛的信息访问相关联，并概述设计在离线先验和在线交互之间明确分配建模能力的检索系统的研究方向。
英文摘要
Modern information retrieval systems typically operationalise relevance as a query-conditional score computed at inference time. This design choice has become dominant such that alternative decompositions of relevance are rarely discussed, despite the long history of document and query priors in probabilistic retrieval and large-scale search. As neural ranking models grow more computationally expensive and retrieval pipelines expand to include multi-stage ranking, recommendation, and retrieval-augmented generation, this monolithic view of query-time scoring becomes increasingly limiting. We argue that modern information access systems are more naturally understood as performing approximate posterior inference, in which relevance is refined through a staged combination of query-dependent likelihoods and query-independent priors. We extend classical probabilistic retrieval formalisms to contemporary learned systems and show how explicit likelihood-prior decomposition exposes new opportunities to shift computation offline while disentangling document-level and interaction-level beliefs. We present empirical evidence that incorporating query-independent document utility can complement existing rankers and improve effectiveness with minimal query-time computation (solely score fusion). Concretely, a learned prior improves first-stage retrieval through rank fusion (up to 0.046 nDCG@10 on TREC DL-2019 and 0.029 nDCG@10 on TREC DL-2020) and also improves downstream re-ranking, with the largest gains observed for the LLM re-ranker RankZephyr (up to 0.054 nDCG@10 on TREC DL-2020). Finally, we discuss how this decomposition connects to broader information access and outline research directions for designing retrieval systems that explicitly allocate modelling capacity between offline priors and online interaction.
CommentsSIGIR 2026 Perspectives Track
