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.
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguis- tics: Human Language Technologies, pages 547–564, Online
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BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
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.