Finding the Right Balance: Relevance and Diversity in LLM Retrieval
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 pieces a query requires. Using controlled near-duplicate injection and production-style overlapping chunking, we find that diversification harms relevance, evidence coverage and answer quality on clean pools, but becomes beneficial on multi-evidence tasks when redundancy causes nearest-neighbor retrieval to select repeated passages. We therefore introduce a query-adaptive rul...
| Authors | Guillaume Brouillette, Faustin Kagabo, Usef Faghihi et al. |
|---|---|
| Primary category | cs.IR |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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