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Journal articles on the topic 'Retrieval Augmented Generation (RAG)'

Author: Grafiati

Published: 17 May 2025

Last updated: 25 July 2025

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1

Mishra, Ankit, and Aniket Gupta. "Retrieval Augmented Generation (RAG) Model." International Journal of Research Publication and Reviews 6, no. 6 (2025): 4690–93. https://doi.org/10.55248/gengpi.6.0125.0635.

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Liu, Yicheng. "Retrieval-Augmented Generation: Methods, Applications and Challenges." Applied and Computational Engineering 142, no. 1 (2025): 99–108. https://doi.org/10.54254/2755-2721/2025.kl22312.

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The Retrieval-Augmented Generation (RAG) has been proven to have a promising approach. It can address the limitations of purely generative models in knowledge-intensive tasks caused by their reliance on static, pre-trained knowledge. RAG addresses these challenges by integrating a retrieval mechanism with a generative model, enabling dynamic access to external knowledge sources during the generation process. This paper presents a comprehensive study of the RAG framework, focusing on its architecture, training strategies, and applications. The framework combines a dense passage retriever (DPR)

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Long, Xinwei, Zhiyuan Ma, Ermo Hua, Kaiyan Zhang, Biqing Qi, and Bowen Zhou. "Retrieval-Augmented Visual Question Answering via Built-in Autoregressive Search Engines." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24723–31. https://doi.org/10.1609/aaai.v39i23.34653.

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Retrieval-augmented generation (RAG) has emerged to address the knowledge-intensive visual question answering (VQA) task. Current methods mainly employ separate retrieval and generation modules to acquire external knowledge and generate answers, respectively. We propose ReAuSE, an alternative to the previous RAG model for the knowledge-based VQA task, which seamlessly integrates knowledge retriever into the generative multi-modal large language model, serving as a built-in search engine. Specifically, our model functions both as a generative retriever and an accurate answer generator. It not o

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Zhang, Yangxiao. "A Retrieval-augmented Generation Framework with Retriever and Generator Modules for Enhancing Factual Consistency." Applied and Computational Engineering 166, no. 1 (2025): 149–55. https://doi.org/10.54254/2755-2721/2025.tj24496.

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Large Language Models (LLMs) are powerful but often produce factually incorrect content (hallucinations), limiting their reliability in knowledge-intensive tasks. Retrieval-augmented generation (RAG) is a promising approach to mitigate this issue by grounding LLM outputs in external knowledge sources. The paper proposes an RAG framework integrating a retriever and generator module to improve factual consistency. The retriever first identifies relevant documents from large-scale datasets, and the generator then produces context-aware responses based on the retrieved evidence. This study evaluat

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Han, Binglan, Teo Susnjak, and Anuradha Mathrani. "Automating Systematic Literature Reviews with Retrieval-Augmented Generation: A Comprehensive Overview." Applied Sciences 14, no. 19 (2024): 9103. http://dx.doi.org/10.3390/app14199103.

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This study examines Retrieval-Augmented Generation (RAG) in large language models (LLMs) and their significant application for undertaking systematic literature reviews (SLRs). RAG-based LLMs can potentially automate tasks like data extraction, summarization, and trend identification. However, while LLMs are exceptionally proficient in generating human-like text and interpreting complex linguistic nuances, their dependence on static, pre-trained knowledge can result in inaccuracies and hallucinations. RAG mitigates these limitations by integrating LLMs’ generative capabilities with the precisi

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Choi, Yein, Sungwoo Kim, Yipene Cedric Francois Bassole, and Yunsick Sung. "Enhanced Retrieval-Augmented Generation Using Low-Rank Adaptation." Applied Sciences 15, no. 8 (2025): 4425. https://doi.org/10.3390/app15084425.

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Recent advancements in retrieval-augmented generation (RAG) have substantially enhanced the efficiency of information retrieval. However, traditional RAG-based systems still encounter challenges, such as high latency in output decision making, the inaccurate retrieval of road traffic-related laws and regulations, and considerable processing overhead in large-scale searches. This study presents an innovative application of RAG technology for processing road traffic-related laws and regulations, particularly in the context of unmanned systems like autonomous driving. Our approach integrates embe

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Grabuloski, Marko, Aleksandar Karadimce, and Anis Sefidanoski. "Enhancing Language Models with Retrieval-Augmented Generation A Comparative Study on Performance." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 22 (April 2, 2025): 272–97. https://doi.org/10.37394/23209.2025.22.23.

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Retrieval-Augmented Generation (RAG) is a powerful technique that enhances the capabilities of Large Language Models (LLMs) by integrating information retrieval with text generation. By accessing and incorporating relevant external knowledge, RAG systems address the limitations of traditional LLMs, such as memory constraints and the inability to access up-to-date information. This research explores the implementation and evaluation of RAG systems, focusing on their potential to improve the accuracy and relevance of LLM responses. It investigates the impact of different LLM types (causal, quest

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Pingua, Bhagyajit, Adyakanta Sahoo, Meenakshi Kandpal, et al. "Medical LLMs: Fine-Tuning vs. Retrieval-Augmented Generation." Bioengineering 12, no. 7 (2025): 687. https://doi.org/10.3390/bioengineering12070687.

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Large language models (LLMs) are trained on huge datasets, which allow them to answer questions from various domains. However, their expertise is confined to the data that they were trained on. In order to specialize LLMs in niche domains like healthcare, various training methods can be employed. Two of these commonly known approaches are retrieval-augmented Generation and model fine-tuning. Five models—Llama-3.1-8B, Gemma-2-9B, Mistral-7B-Instruct, Qwen2.5-7B, and Phi-3.5-Mini-Instruct—were fine-tuned on healthcare data. These models were trained using three distinct approaches: retrieval-aug

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Chen, Jiawei, Hongyu Lin, Xianpei Han, and Le Sun. "Benchmarking Large Language Models in Retrieval-Augmented Generation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 17754–62. http://dx.doi.org/10.1609/aaai.v38i16.29728.

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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, in

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Vaibhav Fanindra Mahajan. "Retrieval-augmented generation: The technical foundation of intelligent AI Chatbots." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 4093–99. https://doi.org/10.30574/wjarr.2025.26.1.1571.

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Retrieval-Augmented Generation (RAG) has emerged as a transformative approach in conversational AI technology, addressing fundamental limitations of traditional chatbot systems. This technical article explores the architecture, mechanisms, and advantages of RAG implementations. Traditional AI chatbots suffer from outdated knowledge bases, hallucination tendencies, and limited context awareness - constraints that RAG effectively overcomes by combining dynamic information retrieval with sophisticated text generation capabilities. The RAG framework operates through a multi-stage process encompass

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Dong, Guanting, Xiaoshuai Song, Yutao Zhu, Runqi Qiao, Zhicheng Dou, and Ji-Rong Wen. "Toward Verifiable Instruction-Following Alignment for Retrieval Augmented Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 22 (2025): 23796–804. https://doi.org/10.1609/aaai.v39i22.34551.

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Following natural instructions is crucial for the effective application of Retrieval-Augmented Generation (RAG) systems. Despite recent advancements in Large Language Models (LLMs), research on assessing and improving instruction-following (IF) alignment within the RAG domain remains limited. To address this issue, we propose VIF-RAG, an automated, scalable, and verifiable synthetic pipeline for instruction-following alignment in RAG systems. We start by manually crafting a minimal set of atomic instructions (100k) through automated processes. To further bridge the gap in instruction-following

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Shaji, Edwin Alex, Jerishab M. Jerishab M, Leya Thomas, M. Viraj Prabhu, and Asst Prof Chinchu M Pillai. "Survey on Speech Recognition and Retrieval-Augmented Generation." International Journal of Advances in Engineering and Management 06, no. 12 (2024): 75–81. https://doi.org/10.35629/5252-06127581.

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Automatic speech recognition (ASR) and retrieval-augmented generation (RAG) systems have seen remarkable progress in handling multilingualism, noise robustness, real-time transcription, and knowledge-intensive tasks. The survey reviews 12 key papers that contribute to advancements in ASR and RAG, covering approaches like end-to-end multilingual models, noise-reduction techniques, and real-time speech processing. It also examines RAG systems that enhance generative models by integrating retrieval mechanisms for improved accuracy in tasks like question answering and summarization. By categorizin

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Yang, Yihe, Xiaoming Li, Hongwei Jin, and Kun Huang. "Advancing Structured Query Processing in Retrieval-Augmented Generation with Generative Semantic Integration." Frontiers in Computing and Intelligent Systems 9, no. 3 (2024): 64–71. http://dx.doi.org/10.54097/z309gx59.

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Retrieval-Augmented Generation (RAG) has become a pivotal approach in enhancing language models by incorporating external knowledge during the text generation process. However, traditional RAG systems often face challenges in processing structured queries, leading to suboptimal integration of retrieved information. In this paper, we introduce a novel method called Generative Semantic Integration (GSI), which advances structured query processing within RAG frameworks. GSI leverages generative models to semantically integrate structured queries with retrieved data, enabling more coherent and con

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Zhu, Jia, Hanghui Guo, Weijie Shi, Zhangze Chen, and Pasquale De Meo. "RaDIO: Real-Time Hallucination Detection with Contextual Index Optimized Query Formulation for Dynamic Retrieval Augmented Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 26129–37. https://doi.org/10.1609/aaai.v39i24.34809.

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The Dynamic Retrieval Augmented Generation (RAG) paradigm actively decides when and what to retrieve during the text generation process of Large Language Models (LLMs). However, current dynamic RAG methods fall short in both aspects: identifying the optimal moment to activate the retrieval module and crafting the appropriate query once retrieval is triggered. To overcome these limitations, we introduce an approach, namely, RaDIO, Real-Time Hallucination Detection with Contextual Index Optimized query formulation for dynamic RAG. The approach is specifically designed to make decisions on when a

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Mitul Ashvinbhai Trivedi. "Enhancing legal practice through retrieval-augmented generation." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2703–12. https://doi.org/10.30574/wjaets.2025.15.2.0852.

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Retrieval-Augmented Generation (RAG) technology is transforming legal practice by combining sophisticated information retrieval with contextual content generation. As law firms confront mounting document volumes and rising client expectations, RAG systems provide a precision-oriented approach that maintains accuracy while increasing processing speed. This article examines how RAG's dual-component architecture creates distinctive advantages for legal applications through semantic understanding and contextual generation. The technical framework leverages vector databases and advanced language mo

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Rackauckas, Zackary. "Rag-Fusion: A New Take on Retrieval Augmented Generation." International Journal on Natural Language Computing 13, no. 1 (2024): 37–47. http://dx.doi.org/10.5121/ijnlc.2024.13103.

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Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal scores and fusing the documents and scores. Through manually evaluating answers on accuracy, relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and comp

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International, Journal on Natural Language Computing (IJNLC). "Rag-Fusion: A New Take on Retrieval Augmented Generation." International Journal on Natural Language Computing (IJNLC) 13, no. 1 (2024): 37–47. https://doi.org/10.5121/ijnlc.2024.13103.

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Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal scores and fusing the documents and scores. Through manually evaluating answers on accuracy, relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and comp

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Zhang, Qinye. "PromptCraft-RAG: Context-based Prompt Enhancement of Refining Query for Retrieval Augmented Generation." Applied and Computational Engineering 154, no. 1 (2025): 137–44. https://doi.org/10.54254/2755-2721/2025.tj23129.

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Retrieval-augmented generation (RAG) has become a transformative framework in Natural Language Processing (NLP). It contributes to the generation process by retrieving relevant information from external knowledge bases, thus making the responses more accurate and contextualized. Recent developments in RAG have renewed interest in optimizing RAG frameworks, such as improving the efficiency of the retrieval module, query reconstruction, refinement, ranking mechanisms, and resolution of hallucinations. However, RAG still faces significant bottlenecks, especially when it comes to understanding kno

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Lau, David, Ganthan Narayana Samy, Dr Fiza Abdul Rahim, et al. "Multimodal RAG Analysis of Product Datasheet." Open International Journal of Informatics 12, no. 2 (2024): 1–12. https://doi.org/10.11113/oiji2024.12n2.309.

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Large language models such as ChatGPT serves as multipurpose chatbot that can provide information across diverse disciplines. However, in order to generate timely and accurate response, retrieval-augmented generation method has been devised to enhance the response of these models. The release of vision models has paved the way for practitioners to perform multimodal retrieval augmented generation on documents that commonly consist of a combination of text, images and tables. Hence, this method is explored to analyze a product datasheet and match it with minimum specification required by potent

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Vanita, B. Mhaske, and S. Kadam Rhishikesh. "Rag In Research: Exploring The Intersection of Retrieval and Generation." International Journal of Advance and Applied Research S6, no. 22 (2025): 929–35. https://doi.org/10.5281/zenodo.15533920.

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<em>In today’s world of information overload, it’s essential to manage data efficiently and get useful insights from various sources. Retrieval-Augmented Generation (RAG) systems assist by combining methods to retrieve relevant information with models that generate clear and precise answers based on that information. This approach makes it easier to access and understand the information we need. By breaking documents into chunks and embedding them, the system enables fast and targeted retrieval of relevant information from large document collections, saving time compared to reading

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Hartono, Luluk Setiawati, Esther Irawati Setiawan, and Vrijraj Singh. "Retrieval Augmented Generation-Based Chatbot for Prospective and Current University Students." International Journal of Engineering, Science and Information Technology 5, no. 3 (2025): 268–77. https://doi.org/10.52088/ijesty.v5i3.951.

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Universities utilize chatbots as assistants for users, especially prospective and current students, to access information and answer questions with relevant answers. This study introduces a new approach to an open-source model-based QA system using Gemma2-2b-it by combining Retrieval Augmented Generation (RAG) and Fine-tuning (FT) techniques. Previously, some studies have focused on only one approach, but this study will combine and compare both methods separately. Raw conversation data from WhatsApp, the main university website, and university PDF documents are used. The Retrieval Augmented G

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Siddharth Nandagopal. "Securing Retrieval-Augmented Generation Pipelines: A Comprehensive Framework." Journal of Computer Science and Technology Studies 7, no. 1 (2025): 17–29. https://doi.org/10.32996/jcsts.2025.7.1.2.

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Retrieval-Augmented Generation (RAG) has significantly enhanced the capabilities of Large Language Models (LLMs) by enabling them to access and incorporate external knowledge sources, thereby improving response accuracy and relevance. However, the security of RAG pipelines remains a paramount concern as these systems become integral to various critical applications. This paper introduces a comprehensive framework designed to secure RAG pipelines through the integration of advanced encryption techniques, zero-trust architecture, and structured guardrails. The framework employs symmetric and asy

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Yao, Chengyuan, and Satoshi Fujita. "Adaptive Control of Retrieval-Augmented Generation for Large Language Models Through Reflective Tags." Electronics 13, no. 23 (2024): 4643. http://dx.doi.org/10.3390/electronics13234643.

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While retrieval-augmented generation (RAG) enhances large language models (LLMs), it also introduces challenges that can impact accuracy and performance. In practice, RAG can obscure the intrinsic strengths of LLMs. Firstly, LLMs may become too reliant on external retrieval, underutilizing their own knowledge and reasoning, which can diminish responsiveness. Secondly, RAG may introduce irrelevant or low-quality data, adding noise that disrupts generation, especially with complex tasks. This paper proposes an RAG framework that uses reflective tags to manage retrieval, evaluating documents in p

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Ajay Mukund, S., and K. S. Easwarakumar. "Optimizing Legal Text Summarization Through Dynamic Retrieval-Augmented Generation and Domain-Specific Adaptation." Symmetry 17, no. 5 (2025): 633. https://doi.org/10.3390/sym17050633.

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Legal text summarization presents distinct challenges due to the intricate and domain-specific nature of legal language. This paper introduces a novel framework integrating dynamic Retrieval-Augmented Generation (RAG) with domain-specific adaptation to enhance the accuracy and contextual relevance of legal document summaries. The proposed Dynamic Legal RAG system achieves a vital form of symmetry between information retrieval and content generation, ensuring that retrieved legal knowledge is both comprehensive and precise. Using the BM25 retriever with top-3 chunk selection, the system optimiz

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Xu, Kehan, Kun Zhang, Jingyuan Li, Wei Huang, and Yuanzhuo Wang. "CRP-RAG: A Retrieval-Augmented Generation Framework for Supporting Complex Logical Reasoning and Knowledge Planning." Electronics 14, no. 1 (2024): 47. https://doi.org/10.3390/electronics14010047.

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The Retrieval-Augmented Generation (RAG) framework enhances Large Language Models (LLMs) by retrieving relevant knowledge to broaden their knowledge boundaries and mitigate factual hallucinations stemming from knowledge gaps. However, the RAG Framework faces challenges in effective knowledge retrieval and utilization; invalid or misused knowledge will interfere with LLM generation, reducing reasoning efficiency and answer quality. Existing RAG methods address these issues by decomposing and expanding queries, introducing special knowledge structures, and using reasoning process evaluation and

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James, Antony, Marcello Trovati, and Simon Bolton. "Retrieval-Augmented Generation to Generate Knowledge Assets and Creation of Action Drivers." Applied Sciences 15, no. 11 (2025): 6247. https://doi.org/10.3390/app15116247.

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This article explores the application of Retrieval-Augmented Generation (RAG) to enhance the creation of knowledge assets and develop actionable insights from complex datasets. It begins by contextualising the limitations of large language models (LLMs), notably their knowledge cut-offs and hallucination tendencies, and it will present RAG as a promising solution that integrates external knowledge retrieval to improve factual accuracy and relevance. This study reviews current RAG architectures, including naïve and advanced models, emphasising techniques such as optimised indexing, query refine

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Zhang, Wan, and Jing Zhang. "Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review." Mathematics 13, no. 5 (2025): 856. https://doi.org/10.3390/math13050856.

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Retrieval-augmented generation (RAG) leverages the strengths of information retrieval and generative models to enhance the handling of real-time and domain-specific knowledge. Despite its advantages, limitations within RAG components may cause hallucinations, or more precisely termed confabulations in generated outputs, driving extensive research to address these limitations and mitigate hallucinations. This review focuses on hallucination in retrieval-augmented large language models (LLMs). We first examine the causes of hallucinations from different sub-tasks in the retrieval and generation

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Pokhrel, Sangita, Bina K C, and Prashant Bikram Shah. "A Practical Application of Retrieval-Augmented Generation for Website-Based Chatbots: Combining Web Scraping, Vectorization, and Semantic Search." Journal of Trends in Computer Science and Smart Technology 6, no. 4 (2025): 424–42. https://doi.org/10.36548/jtcsst.2024.4.007.

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The Retrieval-Augmented Generation (RAG) model significantly enhances the capabilities of large language models (LLMs) by integrating information retrieval with text generation, which is particularly relevant for applications requiring context-aware responses based on dynamic data sources. This research study presents a practical implementation of a RAG model personalized for a Chabot that answers user inquiries from various specific websites. The methodology encompasses several key steps: web scraping using BeautifulSoup to extract relevant content, text processing to segment this content int

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Yoon, Yeochan, and Sookyun Kim. "Trends and Prospects of Retrieval-Augmented Generation (RAG) for Generative AI." Journal of Korean Association of Computer Education 28, no. 2 (2025): 69–80. https://doi.org/10.32431/kace.2025.28.2.007.

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Gresha Bhatia. "Intelligent Railways: Leveraging Retrieval-Augmented Generation for Smarter Systems." Communications on Applied Nonlinear Analysis 32, no. 3s (2024): 91–103. https://doi.org/10.52783/cana.v32.2553.

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In an era for faster, secure and convenient mode of travel, there is a need for a system that provides real time updates This paper presents a technical study on the integration of Retrieval-Augmented Generation (RAG) systems within railway operations, emphasizing their potential to enhance decision-making, service delivery, and passenger engagement. The study explores how RAG systems can streamline processes by providing accurate, context-aware responses to inquiries across various railway services, including ticketing, scheduling, and customer support. The findings highlight key challenges s

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O, Nakhod. "Using retrieval-augmented generation to elevate low-code developer skills." Artificial Intelligence 28, AI.2023.28(3) (2023): 126–30. http://dx.doi.org/10.15407/jai2023.03.126.

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This article proposes applying retrieval-augmented generation (RAG) to improve the skills of low-code developers by augmenting large language models with up-to-date domain-specific knowledge. As low-code development requires combining multiple systems into a final product, developers must consult several sources of documentation and various articles, videos, and forum threads. Such a process may be time-consuming, prompting the use of an LLM for the authoritative answer. However, LLMs often lack knowledge of low-code platforms, leading to hallucinations and superficial responses. RAG utilizes

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Tureniyazova, Asiya Ibragimovna, and Kirill Vladimirovich Sprishevskiy. "OPTIMIZING RAG SYSTEMS WITH FINE-TUNING TECHNIQUES." Educational Research in Universal Sciences 3, no. 3 (2024): 102–11. https://doi.org/10.5281/zenodo.10836637.

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<em>This study explores the optimization of Retrieval-Augmented Generation (RAG) systems through fine-tuning techniques in natural language processing. Results demonstrate significant improvements in accuracy, relevance, and efficiency. Fine-tuning enhances RAG models' adaptability to specific tasks or domains, paving the way for transformative advancements in information retrieval and content generation.</em>

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Gu, Jiafeng. "A Research of Challenges and Solutions in Retrieval Augmented Generation (RAG) Systems." Highlights in Science, Engineering and Technology 124 (February 18, 2025): 132–38. https://doi.org/10.54097/364hex16.

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Retrieval-Augmented Generation (RAG) systems represent a significant innovation in the field of Natural Language Processing (NLP), ingeniously integrating Large Language Models (LLMs) with dynamic external knowledge retrieval. This amalgamation not only enhances the models' responsiveness to real-world knowledge but also addresses the limitations of conventional generative models in terms of knowledge update velocity and factual accuracy. This review examines the challenges faced by RAG systems and their solutions. It delves into the central architecture of RAG systems, encompassing retrieval

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Zhu, Xishi, Xiaoming Guo, Shengting Cao, Shenglin Li, and Jiaqi Gong. "StructuGraphRAG: Structured Document-Informed Knowledge Graphs for Retrieval-Augmented Generation." Proceedings of the AAAI Symposium Series 4, no. 1 (2024): 242–51. http://dx.doi.org/10.1609/aaaiss.v4i1.31798.

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Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external data sources beyond their training sets and querying predefined knowledge bases to generate accurate, context-rich responses. Most RAG implementations use vector similarity searches, but the effectiveness of this approach and the representation of knowledge bases remain underexplored. Emerging research suggests knowledge graphs as a promising solution. Therefore, this paper presents StructuGraphRAG, which leverages document structures to inform the extraction process and constructs knowledge gr

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Knollmeyer, Simon, Oğuz Caymazer, and Daniel Grossmann. "Document GraphRAG: Knowledge Graph Enhanced Retrieval Augmented Generation for Document Question Answering Within the Manufacturing Domain." Electronics 14, no. 11 (2025): 2102. https://doi.org/10.3390/electronics14112102.

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Retrieval-Augmented Generation (RAG) systems have shown significant potential for domain-specific Question Answering (QA) tasks, although persistent challenges in retrieval precision and context selection continue to hinder their effectiveness. This study introduces Document Graph RAG (GraphRAG), a novel framework that bolsters retrieval robustness and enhances answer generation by incorporating Knowledge Graphs (KGs) built upon a document’s intrinsic structure into the RAG pipeline. Through the application of the Design Science Research methodology, we systematically design, implement, and ev

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Amugongo, Lameck Mbangula, Pietro Mascheroni, Steven Brooks, Stefan Doering, and Jan Seidel. "Retrieval augmented generation for large language models in healthcare: A systematic review." PLOS Digital Health 4, no. 6 (2025): e0000877. https://doi.org/10.1371/journal.pdig.0000877.

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Large Language Models (LLMs) have demonstrated promising capabilities to solve complex tasks in critical sectors such as healthcare. However, LLMs are limited by their training data which is often outdated, the tendency to generate inaccurate (“hallucinated”) content and a lack of transparency in the content they generate. To address these limitations, retrieval augmented generation (RAG) grounds the responses of LLMs by exposing them to external knowledge sources. However, in the healthcare domain there is currently a lack of systematic understanding of which datasets, RAG methodologies and e

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Wagenpfeil, Stefan. "Multimedia Graph Codes for Fast and Semantic Retrieval-Augmented Generation." Electronics 14, no. 12 (2025): 2472. https://doi.org/10.3390/electronics14122472.

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Retrieval-Augmented Generation (RAG) has become a central approach to enhance the factual consistency and domain specificity of large language models (LLMs) by incorporating external context at inference time. However, most existing RAG systems rely on dense vector-based similarity, which fails to capture complex semantic structures, relational dependencies, and multimodal content. In this paper, we introduce Graph Codes—a matrix-based encoding of Multimedia Feature Graphs—as an alternative retrieval paradigm. Graph Codes preserve semantic topology by explicitly encoding entities and their typ

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Siriwardhana, Shamane, Rivindu Weerasekera, Elliott Wen, Tharindu Kaluarachchi, Rajib Rana, and Suranga Nanayakkara. "Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering." Transactions of the Association for Computational Linguistics 11 (2023): 1–17. http://dx.doi.org/10.1162/tacl_a_00530.

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Abstract Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized for use in other specialized domains such as healthcare and news. In this paper, we evaluate the impact of joint training of the retriever and generator components of RAG for the task of domain adaptation in ODQA. We propose RAG-end2end, an extension to RAG that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training.

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Mukhopadhyay, Parthasarathi. "Optimizing Retrieval in Libraries through RAG: A Framework." Indian Journal of Information Library and Society 37, no. 1-2 (2024): 6–22. https://doi.org/10.5281/zenodo.14266525.

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This research study shows through demonstration that it is quite possible to develop a low-cost question-answer-based search system for libraries by using open source tools and open source large language models (LLMs). The typical problems associated with LLMs are hallucinations and out-of-date, non-contextual answer generation. Surprisingly, it has been found that the same LLMs can generate better contextualized answers when augmented with a set of relevant documents. This process of augmenting relevant documents in LLMs before the generation of answers is known as retrieval-augmented generat

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Swacha, Jakub, and Michał Gracel. "Retrieval-Augmented Generation (RAG) Chatbots for Education: A Survey of Applications." Applied Sciences 15, no. 8 (2025): 4234. https://doi.org/10.3390/app15084234.

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Retrieval-Augmented Generation (RAG) overcomes the main barrier for the adoption of LLM-based chatbots in education: hallucinations. The uncomplicated architecture of RAG chatbots makes it relatively easy to implement chatbots that serve specific purposes and thus are capable of addressing various needs in the educational domain. With five years having passed since the introduction of RAG, the time has come to check the progress attained in its adoption in education. This paper identifies 47 papers dedicated to RAG chatbots’ uses for various kinds of educational purposes, which are analyzed in

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DeBellis, Michael, Nivedita Dutta, Jacob Gino, and Aadarsh Balaji. "Integrating Ontologies and Large Language Models to Implement Retrieval Augmented Generation." Applied Ontology 19, no. 4 (2024): 389–407. https://doi.org/10.1177/15705838241296446.

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Large language models (LLMs) have captured the imagination of the public and the technical community. As powerful as they are they have problems that prohibit their use for highly skilled users. These issues are hallucinations, bias, black-box reasoning, and lack of domain depth. One of the most popular architectures to alleviate these problems is retrieval augmented generation (RAG). In a RAG architecture, the LLM is utilized to generate vectors and to parse and generate natural language. The knowledge base for a RAG architecture is typically a set of documents focused on a particular type of

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Bazzi, Wafaa. "The Wonders of RAG: Streamlining Knowledge with Advanced Techniques Systematic Literature Review Report." Journal of Neurology Research Reviews & Reports 7, no. 3 (2025): 1–4. https://doi.org/10.47363/jnrrr/2025(7)175.

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The Retrieval-Augmented Generation (RAG) framework enhances Large Language Model (LLM) performance by incorporating external knowledge through information retrieval, addressing inherent limitations in standard LLMs

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Deepak, M., A. Anusha, P. Phanivighnesh, and Dr G. Sreenivasulu. "Langchain-Chat with My PDF." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42403.

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This paper presents a state-of-the-art system that is intended to facilitate natural language interaction with PDF documents. Leveraging the powerful Retrieval-Augmented Generation (RAG) algorithm, the solution seamlessly integrates information retrieval and generative language models to generate precise and context-sensitive responses. The operation starts when a user uploads a PDF file. The system proceeds to process the file, breaking it down into bite-sized text chunks that are kept organized for easy retrieval. Upon the submission of a query by a user, the RAG algorithm locates the most a

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Agarwal, Shubham, Sai Sundaresan, Subrata Mitra, et al. "Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generation." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–28. https://doi.org/10.1145/3725273.

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Retrieval-Augmented Generation (RAG) is often used with Large Language Models (LLMs) to infuse domain knowledge or user-specific information. In RAG, given a user query, a retriever extracts chunks of relevant text from a knowledge base. These chunks are sent to an LLM as part of the input prompt. Typically, any given chunk is repeatedly retrieved across user questions. However, currently, for every question, attention layers in LLMs fully compute the Keys and Values (KVs) repeatedly for the input chunks, as state-of-the-art methods cannot reuse KV-caches when chunks appear at arbitrary locati

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Tanyildiz, Derya, Serkan Ayvaz, and Mehmet Fatih Amasyali. "Enhancing Retrieval-Augmented Generation Accuracy with Dynamic Chunking and Optimized Vector Search." Orclever Proceedings of Research and Development 5, no. 1 (2024): 215–25. https://doi.org/10.56038/oprd.v5i1.516.

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Retrieval-Augmented Generation (RAG) architectures depend on the integration of efficient retrieval and ranking mechanisms to enhance response accuracy and relevance. This study investigates a novel approach to improving the response performance of RAG systems, leveraging dynamic chunking for contextual coherence, Sentence-Transformers (all-mpnet-base-v2) for high-quality embeddings, and cross-encoder-based re-ranking for retrieval refinement. Our evaluation utilizes RAGAS metrics to assess key performance metrics, including faithfulness, relevancy, correctness, and context precision. Empirica

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Dhami, Aatishkumar, and Lagan Goel. "Optimizing retrieval augmented generation pipelines for domain specific applications." International Journal of Research in Modern Engineering & Emerging Technology 13, no. 3 (2025): 55–72. https://doi.org/10.63345/ijrmeet.org.v13.i3.4.

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Retrieval Augmented Generation (RAG) pipelines have emerged as a transformative approach in integrating external knowledge into generative models. However, tailoring these systems to domain-specific applications presents unique challenges, including the handling of specialized vocabularies and intricate contextual nuances. This paper introduces a novel optimization framework for RAG pipelines, emphasizing adaptive retrieval strategies, customized knowledge bases, and fine-tuned generative components. By incorporating domain-tailored filtering mechanisms and dynamically adjusting retrieval para

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Radeva, Irina, Ivan Popchev, Lyubka Doukovska, and Miroslava Dimitrova. "Web Application for Retrieval-Augmented Generation: Implementation and Testing." Electronics 13, no. 7 (2024): 1361. http://dx.doi.org/10.3390/electronics13071361.

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The purpose of this paper is to explore the implementation of retrieval-augmented generation (RAG) technology with open-source large language models (LLMs). A dedicated web-based application, PaSSER, was developed, integrating RAG with Mistral:7b, Llama2:7b, and Orca2:7b models. Various software instruments were used in the application’s development. PaSSER employs a set of evaluation metrics, including METEOR, ROUGE, BLEU, perplexity, cosine similarity, Pearson correlation, and F1 score, to assess LLMs’ performance, particularly within the smart agriculture domain. The paper presents the resu

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Sarat Kiran. "Hybrid Retrieval-Augmented Generation (RAG) Systems with Embedding Vector Databases." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2694–702. https://doi.org/10.32628/cseit25112702.

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This article explores the integration of embedding vector databases into Retrieval-Augmented Generation (RAG) systems to enhance the capabilities of large language models. The article explores how hybrid retrieval strategies combining dense vector search with traditional keyword-based methods can address the limitations of standalone LLMs, particularly regarding knowledge cutoff, hallucinations, and access to domain-specific information. The article presents a comprehensive framework covering theoretical foundations, methodological approaches, implementation considerations, and experimental re

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Jeong, Minbyul, Jiwoong Sohn, Mujeen Sung, and Jaewoo Kang. "Improving medical reasoning through retrieval and self-reflection with retrieval-augmented large language models." Bioinformatics 40, Supplement_1 (2024): i119—i129. http://dx.doi.org/10.1093/bioinformatics/btae238.

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Abstract Summary Recent proprietary large language models (LLMs), such as GPT-4, have achieved a milestone in tackling diverse challenges in the biomedical domain, ranging from multiple-choice questions to long-form generations. To address challenges that still cannot be handled with the encoded knowledge of LLMs, various retrieval-augmented generation (RAG) methods have been developed by searching documents from the knowledge corpus and appending them unconditionally or selectively to the input of LLMs for generation. However, when applying existing methods to different domain-specific proble

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Zhu, Yutao, Zhaoheng Huang, Zhicheng Dou, and Ji-Rong Wen. "One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 26166–74. https://doi.org/10.1609/aaai.v39i24.34813.

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Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs) for generating more factual, accurate, and up-to-date content. Existing methods either optimize prompts to guide LLMs in leveraging retrieved information or directly fine-tune LLMs to adapt to RAG scenarios. Although fine-tuning can yield better performance, it often compromises the LLMs' general generation capabilities by modifying their parameters. This limitation poses challenges in practical applications, especially when LLMs are already deployed, as parameter adjustments may affect their origin

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