# DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation Source: [DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:50:54.619Z Authors: Hsin-Ling Hsu, Jengnan Tzeng Published: 2025-03-29T08:35:01.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Video Retrieval on V-RAGBench](): Video Retrieval; V-RAGBench; Recall@5: 46. - [Video Generation on V-RAGBench](): Video Generation; V-RAGBench; Pass Rate (Qwen3-VL-8B): 22.3. ## Original sources and implementation [Original paper]() [PDF]()