TL;DR
Bag-of-Documents Specificity (BoDS) is introduced, a two-stage approach that operationalizes query specificity as intent coherence : how tightly a query’s engaged documents cluster in embedding space using the mean resultant length of a query’s associated document vectors.
This approach is pre-retrieval and query-centric, making it robust in heterogeneous, case-like corpora and applicable across domains such as legal, healthcare, finance, and enterprise search.
It is found that expanded queries that are closer to the ideal expanded query tend to achieve higher retrieval effectiveness and it is shown that the separability of relevant and non-relevant documents provides a complementary perspective for understanding QE performance.
Large language models can broaden query semantics but may introduce unsupported concepts and semantic drift when expanding short or ambiguous queries. To control this problem, this paper develops a feedback-grounded and intent-preserving query expansion method. The query encoder represents the original query, while top...
Retrieval diversification is widely available in retrieval-augmented generation (RAG) frameworks, yet prior studies disagree on whether it improves retrieval and answer quality. We show that its effectiveness varies primarily with candidate-pool redundancy, in a pattern consistent with the number of distinct evidence p...
This study empirically evaluates the robustness of an IR model to the addition of non-relevant documents by merging two collections with negligible topic overlap and finds that MDA is more effective than MDD for retrieval, whereas MDD and MDA rerankers are equally effective.
The main implication of this research is the validation of a practical architecture for improving IR systems, offering a viable alternative for domain-specific contexts such as Sequran.
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