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Query Rewriting for Retrieval-Augmented Large Language Models

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arxiv 2305.14283 v3 pith:EOQJIZ2G submitted 2023-05-23 cs.CL

Xinbei Ma , Yeyun Gong , Pengcheng He , Hai Zhao , Nan Duan This is my paper
classification cs.CL
keywords queryframeworklanguagereaderretrieval-augmentedblack-boxlargellms
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Large Language Models (LLMs) play powerful, black-box readers in the retrieve-then-read pipeline, making remarkable progress in knowledge-intensive tasks. This work introduces a new framework, Rewrite-Retrieve-Read instead of the previous retrieve-then-read for the retrieval-augmented LLMs from the perspective of the query rewriting. Unlike prior studies focusing on adapting either the retriever or the reader, our approach pays attention to the adaptation of the search query itself, for there is inevitably a gap between the input text and the needed knowledge in retrieval. We first prompt an LLM to generate the query, then use a web search engine to retrieve contexts. Furthermore, to better align the query to the frozen modules, we propose a trainable scheme for our pipeline. A small language model is adopted as a trainable rewriter to cater to the black-box LLM reader. The rewriter is trained using the feedback of the LLM reader by reinforcement learning. Evaluation is conducted on downstream tasks, open-domain QA and multiple-choice QA. Experiments results show consistent performance improvement, indicating that our framework is proven effective and scalable, and brings a new framework for retrieval-augmented LLM.

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Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings

    cs.CV 2026-04 unverdicted novelty 6.5 of 10

    Using a structured rewrite instead of CoT as the generative interface improves MLLM-based multimodal embedding performance while cutting thinking tokens by about half.

  2. Shared Organizational Memory for Enterprise Coding Agents: System Design and Deployment Snapshot

    cs.AI 2026-07 conditional novelty 6.0 of 10

    A production deployment snapshot shows hook-based automatic capture and LLM curation turning 900 agent learnings into 1,144 shared question-answer memories, with no evidence yet of retrieval or coding-task benefit.

  3. SGIC: A Self-Guided Iterative Calibration Framework for RAG

    cs.CL 2025-06 conditional novelty 6.0 of 10

    SGIC feeds a model's own uncertainty scores back into its prompt for several calibration rounds and improves RAG accuracy on HotpotQA, NQ, and GSM8K.

  4. Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A generator trained with a three-objective discriminator and multi-objective preference alignment improves bidword generation for e-commerce search, improving offline retrieval metrics and online ad revenue.

  5. EvdCLIP: Improving Vision-Language Retrieval with Entity Visual Descriptions from Large Language Models

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A retrieval pipeline that augments CLIP queries with LLM-written entity visual descriptions, selected by a retriever-trained rewriter, improves image-text retrieval over CLIP baselines.

  6. T$^2$: An Adaptive Test-Time Scaling Strategy for Contextual Question Answering

    cs.CL 2025-05 conditional novelty 6.0 of 10

    An adaptive test-time scaling method that selects reasoning strategies based on synthetically generated similar questions improves ROUGE-L on seven CQA benchmarks while claiming lower token use.

  7. BugRepro: Enhancing Android Bug Reproduction with Domain-Specific Knowledge Integration

    cs.SE 2025-05 reject novelty 6.0 of 10

    BugRepro combines RAG with UI transition graphs to improve LLM-based Android bug reproduction, reporting 96/151 success versus 55/151 for AdbGPT, though the evaluation may be contaminated by data overlap.

  8. SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration

    cs.AI 2026-07 conditional novelty 5.0 of 10

    A multi-agent web-search framework that stores progress in shared evidence, coverage, and failure state reports the best F1 scores among compared baselines on WideSearch (80.3 item F1) and GISA (76.5 set F1).

  9. QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting

    cs.CL 2025-08 conditional novelty 5.0 of 10

    A contextual bandit that chooses among five query-rewrite strategies, conditioned on 17 linguistic features, reduces LLM hallucination on QA benchmarks and beats static prompting and no-rewrite baselines.

  10. Question Decomposition for Retrieval-Augmented Generation

    cs.CL 2025-07 conditional novelty 4.0 of 10

    Splitting multi-hop questions into subquestions and reranking the merged retrieval pool improves RAG evidence coverage and answer accuracy on MultiHop-RAG and HotpotQA.

  11. Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation

    cs.CL 2025-06 conditional novelty 4.0 of 10

    Omni-RAG, a query-rewriting and decomposition pipeline on top of standard retrieval and reranking, achieved rank 2 in the SIGIR 2025 LiveRAG Challenge.

  12. Automatic Dataset Generation for Knowledge Intensive Question Answering Tasks

    cs.CL 2025-05 reject novelty 3.0 of 10

    Fine-tuning Llama-3-8b and Mistral-7b-v0.3 on LLM-generated QA pairs from IBM Technotes can improve no-context QA scores over training on human-annotated TechQA data, but the evaluation may be inflated by test-documen...

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