OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation
Domain-specific finetuning is essential for dense retrievers, yet not all data pairs contribute equally to the learning process. We introduce OPERA, a data pruning framework that exploits this heterogeneity to improve both the effectiveness and efficiency of retrieval model adaptation. We first investigate static pruning (SP), which retains only high-similarity query-document pairs, revealing an intrinsic quality-coverage tradeoff: ranking (NDCG) improves while retrieval (Recall) can degrade due to reduced query diversity. To resolve this tradeoff, we propose a two-stage dynamic pruning (DP) strategy that adaptively modulates sampling probabilities at both query and document levels throug...
| Authors | Haoyang Fang, Shuai Zhang, Yifei Ma et al. |
|---|---|
| Primary category | cs.IR |
| Published | 17 March 2026 |
| Last updated at source | 30 July 2026 |
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