BM25 — why is it still competitive with modern retrievers?
mediumAnswer
- Okapi BM25 = probabilistic TF-IDF with length normalization + tunable (TF saturation) + b (length norm).
- Very fast (inverted index), well-understood, competitive on exact-match / keyword-heavy queries where dense embeddings miss.
- Modern practice: hybrid retrieval BM25 + dense embeddings + RRF (reciprocal rank fusion) = best-of-both.
- Standard in Elasticsearch, Vespa, Qdrant hybrid mode, all modern RAG pipelines.
Check yourself — multiple choice
- Outdated
- Probabilistic TF-IDF with length norm; still competitive on keyword queries; hybrid BM25 + dense + RRF = modern retrieval standard
- Random
- Same as LSA
BM25: probabilistic TF-IDF; hybrid with dense embeddings is modern default.
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