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

2025-03-29

AI Summary

In retrieval-augmented generation (RAG), fixed-weight fusion of dense and sparse (BM25) retrieval degrades query adaptability. To address this, we propose a dynamic alpha tuning framework that leverages an LLM-driven, single-result effectiveness assessment mechanism to generate optimal fusion weights between dense and sparse retrievers at the query level. Departing from static weighting paradigms, our approach employs normalized dynamic weighting for lightweight, efficient adaptation. Experiments demonstrate statistically significant improvements over fixed-weight baselines across multiple metrics—including Recall@K and Mean Reciprocal Rank (MRR)—while maintaining high robustness and low inference overhead on medium- and small-scale models. The method enhances retrieval accuracy and end-to-end RAG performance without introducing substantial computational cost.

Technology Category

• Search and Optimization: Algorithm Configuration
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Learning Preferences or Rankings

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Dynamic balance between dense and sparse retrieval methods
• Adaptive weighting for different queries in hybrid retrieval
• Improving retrieval effectiveness using LLM-evaluated scores

Innovation

• Dynamic Alpha Tuning balances dense and sparse retrieval
• LLM evaluates top-1 results for effectiveness scores
• Normalization calibrates optimal adaptive weighting per query