Robustness of IR Models to Collection Growth
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 broadl...
| Authors | Emmanouil Georgios Lionis, Sean MacAvaney, Debasis Ganguly |
|---|---|
| Primary category | cs.IR |
| Published | 24 August 2026 |
| Last updated at source | 25 August 2026 |
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