pith:SPRZFPCV
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
A benchmark with 18 diverse datasets shows BM25 as a robust zero-shot baseline while re-ranking models lead in performance at higher cost.
arxiv:2104.08663 v4 · 2021-04-17 · cs.IR · cs.AI · cs.CL
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Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs. In contrast, dense and sparse-retrieval models are computationally more efficient but often underperform other approaches.
The 18 selected datasets provide a sufficiently heterogeneous and representative sample of out-of-distribution scenarios to draw general conclusions about model generalization in information retrieval.
BEIR is a heterogeneous zero-shot benchmark showing BM25 as a robust baseline while re-ranking and late-interaction models perform best on average at higher cost, with dense and sparse models lagging in generalization.
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| First computed | 2026-07-05T03:24:29.177026Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/SPRZFPCVGD7CGFD2YDZTOSG6VP \
| jq -c '.canonical_record' \
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Canonical record JSON
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