OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation

1. 1Investigate static pruning (SP) to retain high-similarity query-document pairs
2. 2Analyze the quality-coverage tradeoff in static pruning
3. 3Implement OPERA, an online data pruning framework, to adapt retrieval models efficiently

Machine learning researchers and engineers on a team can benefit from OPERA to improve the efficiency of their retrieval models, while product managers can utilize the framework to optimize model performance

💡 Data pruning can improve both effectiveness and efficiency of retrieval model adaptation

🚀 OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation