The study investigated the impact of query rewriting on Retrieval-Augmented Generation (RAG) systems. Four rewriting strategies (S1-S4) were compared against two strong LLM baselines (HyDE, Query2Doc) across three datasets: HotpotQA, AmbigNQ, and the 512K-document EnterpriseRAG-Bench. The research found that rewriting alone was at best competitive with a strong baseline. However, combining methods resulted in outsized gains, particularly when a post-hoc union of four methods (S1+S3+S4+HyDE) improved HIT@10 by +12.5 points on enterprise data.
Analysis revealed that different rewriting strategies performed differently on various questions. The union of five methods reached 52.98 on the EnterpriseRAG-Bench dataset. Budget-matched controls captured only approximately 40% of this gain, indicating that complementarity was the primary driver of the improvement. On HotpotQA, the union added +1.6 to +1.8 points, saturating the all-method oracle, while on AmbigNQ, the same fusion resulted in a decrease (-2.4 below the best solo).
To mitigate the cost of rewriting, a confidence-gated router was evaluated. This router applied rewriting only when the baseline's top-1 score was low, capturing approximately half of the enterprise full-merge gain (+4.3 HIT@10) while reducing rewriting cost to less than 40% of queries. A downstream answer-quality evaluation confirmed the router improved F1 by +1.92 at roughly 40% of the expansion cost.
The findings suggest treating query rewriting as a complementary coverage source, applied through cost-aware routing, rather than a standalone replacement for a strong baseline.
Source: https://arxiv.org/abs/2609.05637