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This study investigates the robustness of Information Retrieval (IR) models when non-relevant documents are added to a collection, a common scenario in dynamic data environments. By classifying models into Multi-Document-Agnostic (MDA) and Multi-Document-Dependent (MDD) categories, the research empirically evaluates their performance degradation in the face of irrelevant data. The findings reveal that while both model types suffer from performance loss, MDA models outperform MDD models in retrieval tasks, suggesting a nuanced approach to model selection in dynamic collections is necessary.
MDA models outperform MDD models in dynamic collections, challenging assumptions about document dependence in IR systems.
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.
