研究检索模型在新增无关文档时的稳定性，发现无依赖模型更稳健。
Robustness of IR Models to Collection Growth
- 按是否依赖其他文档将模型分为两类：无依赖（MDA）与有依赖（MDD）
- 无论哪种模型，添加无关文档后性能均下降，但MDA在检索中表现更优
- 适用于关注检索系统长期稳定性的研究人员和工程师
信息检索（IR）系统旨在从文档集合中识别相关文档。实际应用中，文档集合是动态变化的，新文档频繁加入。理想情况下，检索器的有效性不应因加入无关文档而降低。本文形式化了这一概念，并通过合并两个主题重叠极小的集合进行实证评估。我们假设，检索模型对集合中其他文档的依赖方式（如BM25中的IDF或列表式重排序器中的上下文文档）对其在加入无关文档时的鲁棒性至关重要。我们将模型大致分为不依赖其他文档（多文档无关，MDA）和依赖其他文档（多文档依赖，MDD）两类。结果表明，无论是MDD还是MDA模型，都无法完全抵御无关文档的加入，所有模型均出现性能下降。有趣的是，在检索任务中，MDA模型表现优于MDD；而在重排序任务中，两者效果相当。
原文摘要 · Abstract (English)
Information Retrieval (IR) systems seek to identify relevant documents within a collection. In practical applications, collections are dynamic, with documents frequently added. We argue that ideally, a retriever's effectiveness should not decrease when non-relevant documents are added to a collection. This study formalises this concept and empirically evaluates it by merging two collections with negligible topic overlap. We hypothesise that the way an IR model conditions its ranking on other documents in a collection (e.g., the IDF component in BM25 or contextual documents in listwise rerankers) plays an important role in its robustness to the addition of non-relevant documents. We broadly classify models as those that do not depend on other documents (Multi-Document-Agnostic, MDA) and those that do (Multi-Document-Dependent, MDD). Our results show that neither MDD nor MDA models are fully robust to the addition of non-relevant documents, as all models exhibit some performance degradation. Interestingly, among the models we test, MDA is more effective than MDD for retrieval, whereas MDD and MDA rerankers are equally effective.
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