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Robustness of IR Models to Collection Growth
August 24, 2026 ยท Grace Period ยท ๐ CIKM 2026
Authors
Emmanouil Georgios Lionis, Debasis Ganguly, Sean MacAvaney
arXiv ID
2608.23419
Category
cs.IR: Information Retrieval
Cross-listed
cs.CL
Citations
0
Venue
CIKM 2026
Abstract
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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