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RaFe: Ranking Feedback Improves Query Rewriting for RAG
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As Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved, query rewriting has been widely incorporated into the RAG system for downstream tasks like open-domain QA. Many works have attempted to utilize small models with reinforcement learning rather than costly LLMs to improve query rewriting. However, current methods require annotations (e.g., labeled relevant documents or downstream answers) or predesigned rewards for feedback, which lack generalization, and fail to utilize signals tailored for query rewriting. In this paper, we propose ours, a framework for training query rewriting models free of annotations. By leveraging a publicly available reranker, ours~provides feedback aligned well with the rewriting objectives. Experimental results demonstrate that ours~can obtain better performance than baselines.
Forward citations
Cited by 4 Pith papers
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How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models
On a new benchmark of post-April 2025 queries, LLM rerankers show a 5-15% performance drop compared with familiar benchmarks, and lightweight models match them on efficiency and sometimes accuracy.
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Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation
A bidirectional RL framework jointly optimizes LLM query and document augmentation and improves retrieval accuracy on BEIR benchmarks beyond query-only rewriting.
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SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer
SEFRQO claims a self-evolving fine-tuned LLM with retrieval and execution feedback reduces query latency versus PostgreSQL, but the provided body is a different paper, blocking verification.
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Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation
Omni-RAG, a query-rewriting and decomposition pipeline on top of standard retrieval and reranking, achieved rank 2 in the SIGIR 2025 LiveRAG Challenge.
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