DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2503.23013v1
• Date: Sat, 29 Mar 2025 08:35:01 GMT
• Title: DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation
• Authors: Hsin-Ling Hsu, Jengnan Tzeng,
• Abstract summary: DAT (Dynamic Alpha Tuning) is a novel hybrid retrieval framework that balances dense retrieval and BM25 for each query.<n>It consistently outperforms fixed-weighting hybrid retrieval methods across various evaluation metrics.<n>Even on smaller models, DAT delivers strong performance, highlighting its efficiency and adaptability.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hybrid retrieval techniques in Retrieval-Augmented Generation (RAG) systems enhance information retrieval by combining dense and sparse (e.g., BM25-based) retrieval methods. However, existing approaches struggle with adaptability, as fixed weighting schemes fail to adjust to different queries. To address this, we propose DAT (Dynamic Alpha Tuning), a novel hybrid retrieval framework that dynamically balances dense retrieval and BM25 for each query. DAT leverages a large language model (LLM) to evaluate the effectiveness of the top-1 results from both retrieval methods, assigning an effectiveness score to each. It then calibrates the optimal weighting factor through effectiveness score normalization, ensuring a more adaptive and query-aware weighting between the two approaches. Empirical results show that DAT consistently significantly outperforms fixed-weighting hybrid retrieval methods across various evaluation metrics. Even on smaller models, DAT delivers strong performance, highlighting its efficiency and adaptability.

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