动态调整检索权重，让大模型自动选最合适的搜索方法。
DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation
- 用大模型评估两种检索结果，动态分配权重。
- 在多个指标上显著优于固定权重的混合检索。
- 小模型也能高效使用，适合实际部署场景。
在检索增强生成（RAG）系统中，混合检索通过结合密集检索与稀疏检索（如基于BM25的方法）提升信息获取效果。然而，现有方法因权重固定，难以适应不同查询需求。为此，我们提出DAT（Dynamic Alpha Tuning），一种新型混合检索框架，可针对每个查询动态平衡密集检索与BM25的权重。DAT利用大语言模型（LLM）评估两种检索方法的前1个结果的有效性，并赋予有效性分数；再通过分数归一化校准最优加权因子，实现更自适应、查询感知的权重分配。实验表明，DAT在多种评估指标上均显著优于固定权重的混合检索方法。即使在较小模型上，其表现依然强劲，凸显其高效性与适应性。
原文摘要 · Abstract (English)
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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