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.

翻译：检索增强生成（RAG）系统中的混合检索技术通过结合稠密检索与稀疏检索（例如基于BM25的方法）来增强信息检索能力。然而，现有方法在适应性方面存在不足，固定的权重分配方案难以针对不同查询进行动态调整。为此，我们提出DAT（动态Alpha调节），一种新颖的混合检索框架，能够为每个查询动态平衡稠密检索与BM25检索。DAT利用大语言模型（LLM）评估两种检索方法返回的top-1结果的有效性，并为每个结果分配一个有效性分数。随后，通过有效性分数归一化来校准最优权重因子，从而确保两种方法之间实现更具适应性和查询感知的权重分配。实验结果表明，在各种评估指标上，DAT均持续显著优于固定权重的混合检索方法。即使在较小模型上，DAT仍能表现出强劲性能，突显了其高效性与适应性。