Re-ranking LLMs in Production: Benchmarking Latency vs. Precision

Wed, 05 Mar 2025 10:00:00 +0000

Key question: does the re-ranking precision gain justify the latency cost at each pipeline stage? On a 100ms end-to-end SLA the answer is yes — but only with a quantized in-domain re-ranker, not a stock cross-encoder.

Context: Why Re-rank at All?
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First-stage retrieval (BM25 or dense vectors) is built for speed: you need top-1000 candidates fast. Precision at the very top (position 1–5) is a secondary concern.

Hybrid Retrieval: Combining BM25 and Dense Vectors for Production Search

Wed, 15 Jan 2025 10:00:00 +0000

Key insight: pure BM25 misses semantic matches; pure dense vectors miss exact keywords. Hybrid wins on both MRR@10 and NDCG@5 — and the score-fusion choice (RRF over linear interpolation) matters as much as the model choice.

Why Pure BM25 (or Pure Dense) Isn’t Enough
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BM25 is fast, interpretable, and great at exact keyword matching. Dense retrieval (SBERT, DPR) captures semantic similarity but misses precise term overlap. In practice, neither alone covers the full distribution of user queries — especially in a domain-specific corpus.

Retrieval on Rauf Ibishov

Re-ranking LLMs in Production: Benchmarking Latency vs. Precision

Context: Why Re-rank at All? #

Hybrid Retrieval: Combining BM25 and Dense Vectors for Production Search

Why Pure BM25 (or Pure Dense) Isn’t Enough #

Context: Why Re-rank at All?
#

Why Pure BM25 (or Pure Dense) Isn’t Enough
#