Fine-tuned multi-vector retrieval beats dense and sparse models, benchmarks show
tomaarsen · x · 2026-08-26
tomaarsen summarizes his blogpost: fine-tuning models for your own task pays off, and multi-vector (MVE) models are very strong, beating every dense or sparse retrieval model he could find. Two off-the-shelf models scored 67.04 and 61.42, both underperforming. He also notes BM25 looks unusually strong on MIRIAD because its queries were generated from the documents, giving lexical matching a huge advantage. Takeaway: for solid search, fine-tune an MVE model.
Related event: BM25 Beats Some LLM Retrievers on MIRIAD, Sparking Debate(3 posts)→
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