RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation

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   Enhancing LLM Reliability and Efficiency with Query Refinement in Retrieval Augmented Generation

   Aya Mohamed Abd ElHamied, Ahmed A. Aboelfarag, Yasser El-Sonbaty · 2025 · International Conference on Computing: Theory and Applications · Not summarized yet

   Abstract

   Large language models have achieved great success in various natural language processing tasks, yet they still face several serious challenges, such as errors and hallucinations, and high inference cost. The contribution of this work is an extension framework called Refined Query Retrieval Augmented Generation (RQ-RAG) to enhance query handling through rewriting, decomposition, and disambiguation. The algorithm works with a specifically chosen dataset of some 42,810 samples, and search results from DuckDuckGo are used to provide answers that seemingly make sense in context. We are interested in exploiting LLaMA-4 Maverick, a state-of-the-art model with 17B parameters, as their base. We demon…

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   LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering

   Qingfei Zhao, Ruobing Wang, Yukuo Cen et al. · 2024 · Computation and Language · cited 84 times · AI summary available

   Abstract

   Long-Context Question Answering (LCQA), a challenging task, aims to reason over long-context documents to yield accurate answers to questions. Existing long-context Large Language Models (LLMs) for LCQA often struggle with the "lost in the middle" issue. Retrieval-Augmented Generation (RAG) mitigates this issue by providing external factual evidence. However, its chunking strategy disrupts the global long-context information, and its low-quality retrieval in long contexts hinders LLMs from identifying effective factual details due to substantial noise. To this end, we propose LongRAG, a general, dual-perspective, and robust LLM-based RAG system paradigm for LCQA to enhance RAG's understandin…

3. Very close Not relevant

   Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA

   Nirmal Roy, Leonardo F. R. Ribeiro, Rexhina Blloshmi et al. · 2024 · Computation and Language · cited 25 times · AI summary available

   Abstract

   Augmenting Large Language Models (LLMs) with information retrieval capabilities (i.e., Retrieval-Augmented Generation (RAG)) has proven beneficial for knowledge-intensive tasks. However, understanding users' contextual search intent when generating responses is an understudied topic for conversational question answering (QA). This conversational extension leads to additional concerns when compared to single-turn QA as it is more challenging for systems to comprehend conversational context and manage retrieved passages over multiple turns. In this work, we propose a method for enabling LLMs to decide when to retrieve in RAG settings given a conversational context. When retrieval is deemed nec…

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   Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity

   Soyeong Jeong, Jinheon Baek, Sukmin Cho et al. · 2024 · Computation and Language · cited 790 times · AI summary available

   Abstract

   Retrieval-Augmented Large Language Models (LLMs), which incorporate the non-parametric knowledge from external knowledge bases into LLMs, have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA). However, even though there are various approaches dealing with queries of different complexities, they either handle simple queries with unnecessary computational overhead or fail to adequately address complex multi-step queries; yet, not all user requests fall into only one of the simple or complex categories. In this work, we propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augm…

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   Retrieval-Augmented Generation for Large Language Models: A Survey

   Yunfan Gao, Yun Xiong, Xinyu Gao et al. · 2023 · Computation and Language · preprint, not peer reviewed · cited 4,420 times · AI summary available

   Abstract

   Large language models (LLMs) demonstrate powerful capabilities, but they still face challenges in practical applications, such as hallucinations, slow knowledge updates, and lack of transparency in answers. Retrieval-Augmented Generation (RAG) refers to the retrieval of relevant information from external knowledge bases before answering questions with LLMs. RAG has been demonstrated to significantly enhance answer accuracy, reduce model hallucination, particularly for knowledge-intensive tasks. By citing sources, users can verify the accuracy of answers and increase trust in model outputs. It also facilitates knowledge updates and the introduction of domain-specific knowledge. RAG effectivel…

6. Very close Cites this paper Not relevant

   Reasoning Query Optimize for Retrieval Augmented Generation

   Yifan Zhang, Haozhun Jiang, Shuyuan Zhu et al. · 2025 · IEEE International Conference on Network Infrastructure and Digital Content · Not summarized yet

   Abstract

   In recent years, large language models (LLMs) have demonstrated remarkable progress in natural language understanding and generation. However, they continue to face significant challenges, particularly in handling time-sensitive queries and mitigating hallucination issues. The Retrieval-Augmented Generation (RAG) framework has emerged as a promising solution to these challenges by integrating external knowledge retrieval into the generation process. Nevertheless, existing RAG approaches exhibit a critical limitation: they over-rely on the original user query during retrieval, often failing to account for query ambiguity, complexity, and contextual nuances. This suboptimal alignment between r…

7. Very close Not relevant

   RewriteGen: Autonomous Query Optimization for Retrieval-Augmented Large Language Models via Reinforcement Learning

   Yixuan Zhao, Zihao Fan, Ying-Ying Cao et al. · 2026 · Electronics · cited 1 time · Not summarized yet

   Abstract

   Large Language Models (LLMs) have achieved substantial progress in knowledge-intensive tasks, particularly through Retrieval-Augmented Generation (RAG) frameworks. However, existing RAG systems often suffer from performance degradation when input queries are misaligned with retrieval requirements, and effectively coordinating retrieval with reasoning remains challenging—especially for multi-hop questions requiring iterative retrieval steps. To address these challenges, we propose ReWriteGen, a unified framework that integrates query rewriting, retrieval augmentation, and complementary generation within a coordinated architecture, optimized using reinforcement learning (Group Relative Policy …

8. Very close Cites this paper Not relevant

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

   Guan-Ting Dong, Xiaoxi Li, Yu-Yao Zhang et al. · 2025 · arXiv.org · preprint, not peer reviewed · cited 7 times · Not summarized yet

   Abstract

   Real-world live retrieval-augmented generation (RAG) systems face significant challenges when processing user queries that are often noisy, ambiguous, and contain multiple intents. While RAG enhances large language models (LLMs) with external knowledge, current systems typically struggle with such complex inputs, as they are often trained or evaluated on cleaner data. This paper introduces Omni-RAG, a novel framework designed to improve the robustness and effectiveness of RAG systems in live, open-domain settings. Omni-RAG employs LLM-assisted query understanding to preprocess user inputs through three key modules: (1) Deep Query Understanding and Decomposition, which utilizes LLMs with tail…

Prior work it builds on

1. Very close Used in depth by this paper A method it uses Not relevant

   Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

   Akari Asai, Zeqiu Wu, Yizhong Wang et al. · 2023 · Computation and Language · cited 3,006 times · AI summary available

   Abstract

   Despite their remarkable capabilities, large language models (LLMs) often produce responses containing factual inaccuracies due to their sole reliance on the parametric knowledge they encapsulate. Retrieval-Augmented Generation (RAG), an ad hoc approach that augments LMs with retrieval of relevant knowledge, decreases such issues. However, indiscriminately retrieving and incorporating a fixed number of retrieved passages, regardless of whether retrieval is necessary, or passages are relevant, diminishes LM versatility or can lead to unhelpful response generation. We introduce a new framework called Self-Reflective Retrieval-Augmented Generation (Self-RAG) that enhances an LM's quality and fa…

2. Very close A method it uses Not relevant

   Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models

   Wenhao Yu, Hongming Zhang, Xiaoman Pan et al. · 2023 · Computation and Language · cited 263 times · AI summary available

   Abstract

   Retrieval-augmented language models (RALMs) represent a substantial advancement in the capabilities of large language models, notably in reducing factual hallucination by leveraging external knowledge sources. However, the reliability of the retrieved information is not always guaranteed. The retrieval of irrelevant data can lead to misguided responses, and potentially causing the model to overlook its inherent knowledge, even when it possesses adequate information to address the query. Moreover, standard RALMs often struggle to assess whether they possess adequate knowledge, both intrinsic and retrieved, to provide an accurate answer. In situations where knowledge is lacking, these systems …

3. Very close A method it uses Not relevant

   Query Rewriting for Retrieval-Augmented Large Language Models

   Xin-Bei Ma, Yeyun Gong, Pengcheng He et al. · 2023 · arXiv.org · preprint, not peer reviewed · cited 324 times · Not summarized yet

   Abstract

   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 …

4. Very close A method it uses Not relevant

   Recitation-Augmented Language Models

   Zhiqing Sun, Xue-Zhi Wang, Yi Tay et al. · 2022 · International Conference on Learning Representations · cited 91 times · Not summarized yet

   Abstract

   We propose a new paradigm to help Large Language Models (LLMs) generate more accurate factual knowledge without retrieving from an external corpus, called RECITation-augmented gEneration (RECITE). Different from retrieval-augmented language models that retrieve relevant documents before generating the outputs, given an input, RECITE first recites one or several relevant passages from LLMs' own memory via sampling, and then produces the final answers. We show that RECITE is a powerful paradigm for knowledge-intensive NLP tasks. Specifically, we show that by utilizing recitation as the intermediate step, a recite-and-answer scheme can achieve new state-of-the-art performance in various closed-…

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   When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories

   Alex Troy Mallen, Akari Asai, Victor Zhong et al. · 2022 · Annual Meeting of the Association for Computational Linguistics · cited 1,622 times · Not summarized yet

   Abstract

   Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the difficulty of encoding a wealth of world knowledge in their parameters. This paper aims to understand LMs’ strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments on two open-domain entity-centric QA datasets: PopQA, our new dataset with 14k questions about long-tail entities, and EntityQuestions, a widely used open-domain QA dataset. We find that LMs struggle with less popular factual knowledge, and that retrieval augmentation helps significantly in these cases. Scaling, on the oth…

Papers that cite it

1. Very close Uses this paper in depth Not relevant

   Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration

   Ran Xu, Wenqi Shi, Yu-Chen Zhuang et al. · 2025 · arXiv.org · preprint, not peer reviewed · cited 31 times · Not summarized yet

   Abstract

   Retrieval-Augmented Generation (RAG) systems often struggle to handle multi-hop question-answering tasks accurately due to irrelevant context retrieval and limited complex reasoning capabilities. We introduce Collab-RAG, a collaborative training framework that leverages mutual enhancement between a white-box small language model (SLM) and a blackbox large language model (LLM) for RAG. Specifically, the SLM decomposes complex queries into simpler sub-questions, thus enhancing the accuracy of the retrieval and facilitating more effective reasoning by the black-box LLM. Concurrently, the black-box LLM provides feedback signals to improve the SLM's decomposition capability. We observe that Colla…

2. Very close Uses this paper in depth Not relevant

   DMQR-RAG: Diverse Multi-Query Rewriting for RAG

   Zhicong Li, Jia-Hao Wang, Zhishu Jiang et al. · 2024 · arXiv.org · preprint, not peer reviewed · cited 41 times · Not summarized yet

   Abstract

   Large language models often encounter challenges with static knowledge and hallucinations, which undermine their reliability. Retrieval-augmented generation (RAG) mitigates these issues by incorporating external information. However, user queries frequently contain noise and intent deviations, necessitating query rewriting to improve the relevance of retrieved documents. In this paper, we introduce DMQR-RAG, a Diverse Multi-Query Rewriting framework designed to improve the performance of both document retrieval and final responses in RAG. Specifically, we investigate how queries with varying information quantities can retrieve a diverse array of documents, presenting four rewriting strategie…

3. Very close Not relevant

   LightRAG: Simple and Fast Retrieval-Augmented Generation

   Zirui Guo, Lianghao Xia, Yanhua Yu et al. · 2024 · Conference on Empirical Methods in Natural Language Processing · cited 577 times · Not summarized yet

   Abstract

   Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs. However, existing RAG systems have significant limitations, including reliance on flat data representations and inadequate contextual awareness, which can lead to fragmented answers that fail to capture complex inter-dependencies. To address these challenges, we propose LightRAG, which incorporates graph structures into text indexing and retrieval processes. This innovative framework employs a dual-level retrieval system that enhances comprehensive information retrieval from both low-lev…

4. Very close Not relevant

   RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation

   Xuan-Wang Zhang, Yunze Song, Yidong Wang et al. · 2024 · Conference on Empirical Methods in Natural Language Processing · cited 15 times · Not summarized yet

   Abstract

   Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. However, even the most advanced LLMs face challenges such as hallucinations and real-time updating of their knowledge. Current research addresses this bottleneck by equipping LLMs with external knowledge, a technique known as Retrieval Augmented Generation (RAG). However, two key issues constrained the development of RAG. First, there is a growing lack of comprehensive and fair comparisons between novel RAG algorithms. Second, open-source tools such as LlamaIndex and LangChain employ high-level abstractions, which results in a lack of transparency and limits the ability to devel…

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   Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers

   C. Sharma · 2025 · arXiv.org · preprint, not peer reviewed · cited 76 times · Not summarized yet

   Abstract

   Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to enhance large language models (LLMs) by conditioning generation on external evidence retrieved at inference time. While RAG addresses critical limitations of parametric knowledge storage-such as factual inconsistency and domain inflexibility-it introduces new challenges in retrieval quality, grounding fidelity, pipeline efficiency, and robustness against noisy or adversarial inputs. This survey provides a comprehensive synthesis of recent advances in RAG systems, offering a taxonomy that categorizes architectures into retriever-centric, generator-centric, hybrid, and robustness-oriented designs. We systematically anal…