Papers › BM25 Query Augmentation Learned End-to-End
BM25 Query Augmentation Learned End-to-End
Xiaoyin Chen, Sam Wiseman
Given BM25's enduring competitiveness as an information retrieval baseline, we investigate to what extent it can be even further improved by augmenting and re-weighting its sparse query-vector representation. We propose an approach to learning an augmentation and a re-weighting end-to-end, and we find that our approach improves performance over BM25 while retaining its speed. We furthermore find that the learned augmentations and re-weightings transfer well to unseen datasets.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Zero Shot on BEIR (Inference Free Model) | BEIR | BM25 | NCDG@10 | 44.48 | #6 of 6 | Archive leaderboard | report |
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