Retrieval-Augmented Generation for Large Language Models: A Survey

Ranked by how close their title and abstract are to this paper’s. Select some to read them together.

Closest papers

1. Very close Not relevant

   Retrieval-Augmented Generation (RAG) for Large Language Models: A Comprehensive Survey

   T. Chowdhury · 2026 · International Journal on Engineering Artificial Intelligence Management, Decision Support, and Policies · cited 3 times · Not summarized yet

   Abstract

   The Retrieval-Augmented Generation (RAG) paradigm has been proposed as a potent concept that would increase the factual accuracy, reliability, and adaptability of Large Language Models (LLMs) by incorporating external information retrieval and text generation. In comparison to an independent LLM, which leverages solely parametric knowledge, RAG dynamically accesses non-parametric knowledge sources during inference that are up-to-date and are founded on the evidence that was accessed to generate the response. This paper summarizes the foundations of RAG, its architecture, major components (retriever and generator) and an indexing-retrieval-generation methodology. It critically examines retrie…

2. Very close Not relevant

   A Survey of Retrieval-Augmented Generation (RAG) for Large Language Models

   Yu-Song Ma, Hong-Xuan Nie, Chao Chen et al. · 2025 · 2025 International Conference on Trustworthy Big Data and Artificial Intelligence (ICTBAI) · cited 14 times · Not summarized yet

   Abstract

   While Large Language Models (LLMs) are revolutionary, their deployment is constrained by inherent limitations such as factual hallucination and static knowledge. This survey systematically reviews Retrieval-Augmented Generation (RAG), a key paradigm for addressing these challenges by grounding LLMs in external, verifiable knowledge. To overcome the flaws of standalone models, RAG integrates LLMs with updatable knowledge bases, a hybrid approach that significantly enhances output accuracy and trustworthiness. Our primary finding is the technology’s clear evolutionary trajectory, which we structure into three stages: Naive, Advanced, and Modular RAG. This progression demonstrates a shift away …

3. Very close Not relevant

   Research and Implementation of Large Language Model Retrieval-Augmented Generation Technology

   Xuemin Wang, Xinchi Li, Hao Wang · 2025 · 2025 IEEE 3rd International Conference on Control, Electronics and Computer Technology (ICCECT) · Not summarized yet

   Abstract

   With the rapid advancement of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) has emerged as a promising technique to enhance their capabilities. By integrating a retrieval mechanism into traditional generative models, RAG dynamically fetches relevant information from external knowledge bases, thereby improving the accuracy and contextual relevance of generated outputs. This paper delves into the foundational principles and framework of RAG, examining the retrieval-generation process and key modules such as loading, indexing, storage, querying, and evaluation. It also discusses critical technologies underpinning RAG systems, including data partitioning, retrieval algorithm…

4. Very close Not relevant

   Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey

   Ao-Ran Gan, Hao Yu, Kai Zhang et al. · 2025 · arXiv.org · preprint, not peer reviewed · cited 58 times · Not summarized yet

   Abstract

   Recent advancements in Retrieval-Augmented Generation (RAG) have revolutionized natural language processing by integrating Large Language Models (LLMs) with external information retrieval, enabling accurate, up-to-date, and verifiable text generation across diverse applications. However, evaluating RAG systems presents unique challenges due to their hybrid architecture that combines retrieval and generation components, as well as their dependence on dynamic knowledge sources in the LLM era. In response, this paper provides a comprehensive survey of RAG evaluation methods and frameworks, systematically reviewing traditional and emerging evaluation approaches, for system performance, factual a…

5. Very close Not relevant

   Retrieval-augmented generation for natural language processing: a survey

   Shang-Yu Wu, Ying Xiong, Yu-Fei Cui et al. · 2024 · Artificial Intelligence Review · cited 185 times · Not summarized yet

   Abstract

   Large language models (LLMs) have achieved strong empirical performance in various fields, benefiting from their huge amount of parameters that store knowledge. However, LLMs still suffer from several key issues, such as hallucination problems, knowledge update issues, and lacking domain-specific expertise. The appearance of retrieval-augmented generation (RAG), which leverages an external knowledge base to augment LLMs, mitigates these limitations. This paper presents a systematic review of RAG techniques for natural language processing (NLP), with a focus on retrievers and retrieval fusions. We introduce a novel taxonomy of retrieval fusions, such as query-based, logits-based, latent, and …

6. Very close Not relevant

   Retrieval-Augmented Generation: Enhancing Reliability in Large Language Models

   R. Dhenia, R. Sridhar, I. Kanani · 2024 · American International Journal of Computer Science and Technology · Not summarized yet

   Abstract

   Large Language Models (LLMs) have achieved remarkable success in diverse natural language processing tasks, yet a critical challenge remains: the tendency to hallucinate or generate plausible but factually incorrect information. Retrieval-Augmented Generation (RAG) offers a promising approach by combining LLMs with external retrieval systems to ground generation in factual evidence. This paper reviews key developments in RAG architectures, analyzes empirical results demonstrating improved factual accuracy, and discusses technical challenges related to retrieval quality, latency, and adversarial robustness. We further identify open research areas such as scalable retrieval, multi-hop reasonin…

7. Very close Cites this paper Not relevant

   Rethinking Knowledge Retrieval for Generation: A Survey on RAG Architectures and Applications

   Meghana Sunil, V. Shravya, Shravan Venkatraman et al. · 2026 · Not summarized yet

   Abstract

   Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across natural language tasks but remain fundamentally limited by their static knowledge and susceptibility to hallucinations, especially in domains requiring up to date or attribute grounded information. Retrieval Augmented Generation (RAG) addresses these challenges by integrating external retrieval mechanisms with generative models, enabling dynamic, context aware generation grounded in verifiable data sources. This survey presents a comprehensive examination of RAG as a modular and evolving paradigm that enhances factual reliability, adaptability, and task alignment in LLM based systems. We formalize the RA…

8. Very close Not relevant

   A Comprehensive Study-Retrieval-Augmented Generation(RAG)

   S. Kiruba, C. S. Anish, R. Krishna et al. · 2025 · 2025 10th International Conference on Smart Structures and Systems (ICSSS) · Not summarized yet

   Abstract

   Retrieval-Augmented Generation (RAG) which represents a substantial new approach from large language model (LLM) technology by including external, up-to-date knowledge sources as part of the generation process. The hybrid model addresses critical limitations of the LLMs based on simply containing static training data, hallucinations, and a lack of full transparency, by providing the model with pertinent knowledge from dynamic databases or documents to complement the model. The work provides an extensive review of RAG architectures and their core components-retrieval, generation, and augmentation-along with their integration to enhance factual accuracy and contextual relevance. Finally, we di…

Prior work it builds on

1. Very close Used in depth by this paper Not relevant

   Benchmarking Large Language Models in Retrieval-Augmented Generation

   Jiawei Chen, Hongyu Lin, Xianpei Han et al. · 2023 · Computation and Language · cited 732 times · AI summary available

   Abstract

   Retrieval-Augmented Generation (RAG) is a promising approach for mitigating the hallucination of large language models (LLMs). However, existing research lacks rigorous evaluation of the impact of retrieval-augmented generation on different large language models, which make it challenging to identify the potential bottlenecks in the capabilities of RAG for different LLMs. In this paper, we systematically investigate the impact of Retrieval-Augmented Generation on large language models. We analyze the performance of different large language models in 4 fundamental abilities required for RAG, including noise robustness, negative rejection, information integration, and counterfactual robustness…

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

   Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy

   Zhihong Shao, Yeyun Gong, Ye-Long Shen et al. · 2023 · Conference on Empirical Methods in Natural Language Processing · cited 616 times · Not summarized yet

   Abstract

   Large language models are powerful text processors and reasoners, but are still subject to limitations including outdated knowledge and hallucinations, which necessitates connecting them to the world. Retrieval-augmented large language models have raised extensive attention for grounding model generation on external knowledge. However, retrievers struggle to capture relevance, especially for queries with complex information needs. Recent work has proposed to improve relevance modeling by having large language models actively involved in retrieval, i.e., to improve retrieval with generation. In this paper, we show that strong performance can be achieved by a method we call Iter-RetGen, which …

3. Very close A method it uses Not relevant

   CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models

   Yuanjie Lyu, Zhi-Yu Li, Si-Min Niu et al. · 2024 · ACM Trans. Inf. Syst. · cited 153 times · Not summarized yet

   Abstract

   Retrieval-augmented generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This method addresses common LLM limitations, including outdated information and the tendency to produce inaccurate “hallucinated” content. However, evaluating RAG systems is a challenge. Most benchmarks focus primarily on question-answering applications, neglecting other potential scenarios where RAG could be beneficial. Accordingly, in the experiments, these benchmarks often assess only the LLM components of the RAG pipeline or the retriever in knowledge-intensive scenarios, overlooking the impact of external knowledge base construc…

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

   Active Retrieval Augmented Generation

   Zhengbao Jiang, Frank F. Xu, Luyu Gao et al. · 2023 · Computation and Language · cited 955 times · AI summary available

   Abstract

   Despite the remarkable ability of large language models (LMs) to comprehend and generate language, they have a tendency to hallucinate and create factually inaccurate output. Augmenting LMs by retrieving information from external knowledge resources is one promising solution. Most existing retrieval augmented LMs employ a retrieve-and-generate setup that only retrieves information once based on the input. This is limiting, however, in more general scenarios involving generation of long texts, where continually gathering information throughout generation is essential. In this work, we provide a generalized view of active retrieval augmented generation, methods that actively decide when and wh…

5. Very close 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 3,019 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…

Papers that cite it

1. Very close Not relevant

   Enhancing the Precision and Interpretability of Retrieval-Augmented Generation (RAG) in Legal Technology: A Survey

   Mahd Hindi, Linda Mohammed, Ommama Maaz et al. · 2025 · IEEE Access · cited 74 times · Not summarized yet

   Abstract

   Retrieval-Augmented Generation (RAG) is a promising solution that can enhance the capabilities of large language model (LLM) applications in critical domains, including legal technology, by retrieving knowledge from external databases. Implementing RAG pipelines requires careful attention to the techniques and methods implemented in the different stages of the RAG process. However, robust RAG can enhance LLM generation with faithfulness and few hallucinations in responses. In this paper, we discuss the application of RAG in the legal domain. First, we present an overview of the main RAG methods, stages, techniques, and applications in the legal domain. We then briefly discuss the different i…

2. Very close Not relevant

   Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines

   Siru Liu, Allison B. McCoy, Adam T. Wright · 2025 · Journal of the American Medical Informatics Association · cited 201 times · Not summarized yet

   Abstract

   OBJECTIVE: The objectives of this study are to synthesize findings from recent research of retrieval-augmented generation (RAG) and large language models (LLMs) in biomedicine and provide clinical development guidelines to improve effectiveness. MATERIALS AND METHODS: We conducted a systematic literature review and a meta-analysis. The report was created in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 analysis. Searches were performed in 3 databases (PubMed, Embase, PsycINFO) using terms related to "retrieval augmented generation" and "large language model," for articles published in 2023 and 2024. We selected studies that compared baseline LLM per…

3. Very close Not relevant

   A Survey on RAG with LLMs

   Muhammad Arslan, Hussam Ghanem, Saba Munawar et al. · 2024 · Procedia Computer Science · cited 254 times · Not summarized yet

   Abstract

   In the fast-paced realm of digital transformation, businesses are increasingly pressured to innovate and boost efficiency to remain competitive and foster growth. Large Language Models (LLMs) have emerged as game-changers across industries, revolutionizing various sectors by harnessing extensive text data to analyze and generate human-like text. Despite their impressive capabilities, LLMs often encounter challenges when dealing with domain-specific queries, potentially leading to inaccuracies in their outputs. In response, Retrieval-Augmented Generation (RAG) has emerged as a viable solution. By seamlessly integrating external data retrieval into text generation processes, RAG aims to enhanc…

4. Very close Not relevant

   Analyzing performance of Retrieval Augmented Generation models for Question Answering Tasks

   Fazle Mohammed Tasfiq, Sabab Attin, Sheikh Md. Tahmid Hossain et al. · 2026 · Not summarized yet

   Abstract

   Retrieval-Augmented Generation (RAG) models have become almost a cornerstone of open-domain question answering tasks; in which the quality of answers generated in the process, greatly relies on the effectiveness of retrieval mechanisms. In the realm of LLM, the last two years have seen tremendous progress in RAG architectures, with many models reporting to have improved performance through various design innovations. The cultural question and answering systems of South Asia, in particular, present a specific challenge since the region is home to the vast number of languages and traditions, which are closely interrelated. Along with these deeply intertwined historical narratives increase the …

5. Very close Not relevant

   Beyond Hallucination: Content Aware Retrieval Augmented Adaptive Strategies for Knowledge-Boundary Awareness in LLMs

   D. Dhotre · 2026 · Natural Resources for Human Health · Not summarized yet

   Abstract

   Recently the Large language models have demonstrated substantial improvements in natural language processing, understanding and generation. Despite these advances, they remain prone to producing outputs that are fluent yet factually incorrect, a limitation commonly referred to as hallucination. This issue is of particular concern in domains that require high levels of accuracy and reliability. Retrieval-Augmented Generation (RAG) has emerged as an effective framework to address this limitation by incorporating external knowledge sources into the generation process. This review surveys recent research on RAG systems, with an emphasis on methods aimed at mitigating hallucinations through impro…