SAGE: Strategy-Adaptive Generation Engine for Query Rewriting
Teng Wang, Hailei Gong, Changwang Zhang, Jun Wang

TL;DR
This paper introduces SAGE, a strategy-guided reinforcement learning framework for query rewriting that improves retrieval performance, reduces exploration costs, and enhances interpretability by incorporating expert strategies and novel reward mechanisms.
Contribution
The paper presents SAGE, a novel RL-based query rewriting system that integrates expert strategies and innovative reward shaping to outperform existing methods and learn optimal strategies.
Findings
Achieves state-of-the-art NDCG@10 results.
Learns to select optimal strategies and reduces unnecessary exploration.
Generates concise rewrites, lowering inference costs.
Abstract
Query rewriting is pivotal for enhancing dense retrieval, yet current methods demand large-scale supervised data or suffer from inefficient reinforcement learning (RL) exploration. In this work, we first establish that guiding Large Language Models (LLMs) with a concise set of expert-crafted strategies, such as semantic expansion and entity disambiguation, substantially improves retrieval effectiveness on challenging benchmarks, including HotpotQA, FEVER, NFCorpus, and SciFact. Building on this insight, we introduce the Strategy-Adaptive Generation Engine (SAGE), which operationalizes these strategies in an RL framework. SAGE introduces two novel reward shaping mechanisms-Strategic Credit Shaping (SCS) and Contrastive Reward Shaping (CRS)-to deliver more informative learning signals. This strategy-guided approach not only achieves new state-of-the-art NDCG@10 results, but also uncovers…
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Taxonomy
TopicsAdvanced Database Systems and Queries · Scientific Computing and Data Management · Distributed and Parallel Computing Systems
