Bibliografias temáticas / Retrieval Augmented Generation (RAG)

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Literatura científica selecionada sobre o tema "Retrieval Augmented Generation (RAG)"

Autor: Grafiati

Publicado: 17 de maio de 2025

Última modificação: 25 de julho de 2025

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Artigos de revistas sobre o assunto "Retrieval Augmented Generation (RAG)"

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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Mais fontes

Teses / dissertações sobre o assunto "Retrieval Augmented Generation (RAG)"

Schaeffer, Marion. "Towards efficient Knowledge Graph-based Retrieval Augmented Generation for conversational agents." Electronic Thesis or Diss., Normandie, 2025. http://www.theses.fr/2025NORMIR06.

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Les agents conversationnels se sont largement répandus ces dernières années. Aujourd'hui, ils ont dépassé leur objectif initial de simuler une conversation avec un programme informatique et sont désormais des outils précieux pour accéder à l'information et effectuer diverses tâches, allant du service client à l'assistance personnelle. Avec l'essor des modèles génératifs et des grands modèles de langage (LLM), les capacités des agents conversationnels ont été décuplées. Cependant, ils sont désormais sujets à des hallucinations, générant ainsi des informations erronées. Une technique populaire p

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Busatta, Gianluca. "Italian Retrieval-Augmented Generative Question Answering System for Legal Domains." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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A typical scenario involves a user searching an information about something and obtaining a list of documents from an information retrieval system. The retrieved documents may be more or less relevant and it could happen that the information sought is contained in several documents. This would possibly leave the task of searching the information in different documents to the user. In this thesis, it is has been developed an Italian question answering system for legal domains with a Retrieval-Augmented Generation (RAG) approach that aims to directly satisfy the information need of the user. The

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Livros sobre o assunto "Retrieval Augmented Generation (RAG)"

sahota, harpreet. Practical Retrieval Augmented Generation. Wiley & Sons, Incorporated, John, 2024.

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sahota, harpreet. Practical Retrieval Augmented Generation. Wiley & Sons, Incorporated, John, 2024.

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sahota, harpreet. Practical Retrieval Augmented Generation. Wiley & Sons, Incorporated, John, 2024.

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Capítulos de livros sobre o assunto "Retrieval Augmented Generation (RAG)"

Parasuraman, Banu. "Spring AI and RAG (Retrieval-Augmented Generation)." In Mastering Spring AI. Apress, 2024. https://doi.org/10.1007/979-8-8688-1001-5_4.

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Shan, Richard, and Tony Shan. "Retrieval-Augmented Generation Architecture Framework: Harnessing the Power of RAG." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-77954-1_6.

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Ghadekar, Premanand, Shreyash Tekade, Dhawal Sakharwade, Sayee Zanzane, Ankur Tripathi, and Shivam Tiwadi. "Real-time crisis response optimization with Retrieval Augmented Generation (RAG)." In Intelligent Computing and Communication Techniques. CRC Press, 2025. https://doi.org/10.1201/9781003530190-14.

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Babaei Giglou, Hamed, Tilahun Abedissa Taffa, Rana Abdullah, et al. "Scholarly Question Answering Using Large Language Models in the NFDI4DataScience Gateway." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65794-8_1.

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AbstractThis paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing a Retrieval Augmented Generation-based (RAG) approach. The NFDI4DS Gateway, as a foundational framework, offers a unified and intuitive interface for querying various scientific databases using federated search. The RAG-based scholarly QA, powered by a Large Language Model (LLM), facilitates dynamic interaction with search results, enhancing filtering capabilities and fostering a conversational engagement with the Gateway search. The effectiveness of both the Gateway and t

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Holvoet, Laura, Michael van Bekkum, and Aijse de Vries. "An Approach to Automated Instruction Generation with Grounding Using LLMs and RAG." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86489-6_23.

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Abstract Despite ongoing digitization in industry, many companies still work with paper instructions or ‘paper-on-glass’ solutions (e.g., PDF files on screens). In recent years, various digital work instruction (DWI) technologies have become available that provide shop-floor employees with information during their activities, e.g., sequences of instructions for tasks at hand. Engineering new instructions in these systems for new products or product variants is however expensive and time-consuming. To scale up, there is a need for methods to generate work instructions (semi) automatically. Rece

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Jay, Rabi. "Building Advanced Q&A and Search Applications Using Retrieval-Augmented Generation (RAG)." In Generative AI Apps with LangChain and Python. Apress, 2024. https://doi.org/10.1007/979-8-8688-0882-1_7.

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Nazary, Fatemeh, Yashar Deldjoo, and Tommaso di Noia. "Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender Systems." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88717-8_18.

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Pradeep, Ronak, Nandan Thakur, Sahel Sharifymoghaddam, et al. "Ragnarök: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88708-6_9.

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Wiratunga, Nirmalie, Ramitha Abeyratne, Lasal Jayawardena, et al. "CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering." In Case-Based Reasoning Research and Development. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63646-2_29.

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Bu, Kangning, Zehua Wang, Pengcheng Zhu, et al. "RB-RAG: Intelligent Question Answering System of Rock Burst Knowledge Based on Retrieval-Augmented Generation." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-9946-9_10.

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Trabalhos de conferências sobre o assunto "Retrieval Augmented Generation (RAG)"

Mishra, Aniket, Aniket Gupta, and Anil Kumar Sagar. "Retrieval Augmented Generation (RAG) Model." In 2025 First Global Conference on AI Research and Emerging Developments. Ganitara Research Foundation, 2025. https://doi.org/10.63169/gcared2025.p16.

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Tural, Büşra, Zeynep Örpek, and Zeynep Destan. "Retrieval-Augmented Generation (RAG) and LLM Integration." In 2024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS). IEEE, 2024. https://doi.org/10.1109/isas64331.2024.10845308.

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Rappazzo, Brendan Hogan, Yingheng Wang, Aaron Ferber, and Carla Gomes. "GEM-RAG: Graphical Eigen Memories for Retrieval Augmented Generation." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00196.

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Rani, Maneeha, Bhupesh Kumar Mishra, Dhavalkumar Thakker, and Mohammad Nouman Khan. "To Enhance Graph-Based Retrieval-Augmented Generation (RAG) with Robust Retrieval Techniques." In 2024 18th International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2024. https://doi.org/10.1109/icosst64562.2024.10871140.

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Bhat, Vani, Sree Divya Cheerla, Jinu Rose Mathew, Nupur Pathak, Guannan Liu, and Jerry Gao. "Retrieval Augmented Generation (RAG) Based Restaurant Chatbot with AI Testability." In 2024 IEEE 10th International Conference on Big Data Computing Service and Machine Learning Applications (BigDataService). IEEE, 2024. http://dx.doi.org/10.1109/bigdataservice62917.2024.00008.

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Danuarta, Leo, Viny Christanti Mawardi, and Viciano Lee. "Retrieval-Augmented Generation (RAG) Large Language Model For Educational Chatbot." In 2024 Ninth International Conference on Informatics and Computing (ICIC). IEEE, 2024. https://doi.org/10.1109/icic64337.2024.10957676.

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Shelley, Sayuri, Prabhjeet Kaur, Garima Aggarwal, Abhishek Kaushal, and Malay Kishore Dutta. "Flora-RAG: Enhancing Conversational AI with Retrieval Augmented Generation for Floriculture." In 2025 International Conference on Engineering, Technology & Management (ICETM). IEEE, 2025. https://doi.org/10.1109/icetm63734.2025.11051979.

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Sawarkar, Kunal, Abhilasha Mangal, and Shivam Raj Solanki. "Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers." In 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2024. http://dx.doi.org/10.1109/mipr62202.2024.00031.

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Agrawal, Garima, Tharindu Kumarage, Zeyad Alghamdi, and Huan Liu. "Mindful-RAG: A Study of Points of Failure in Retrieval Augmented Generation." In 2024 2nd International Conference on Foundation and Large Language Models (FLLM). IEEE, 2024. https://doi.org/10.1109/fllm63129.2024.10852457.

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Cai, Yucheng, Si Chen, Yuxuan Wu, Yi Huang, Junlan Feng, and Zhijian Ou. "The 2nd Futuredial Challenge: Dialog Systems With Retrieval Augmented Generation (Futuredial-RAG)." In 2024 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2024. https://doi.org/10.1109/slt61566.2024.10832299.

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Relatórios de organizações sobre o assunto "Retrieval Augmented Generation (RAG)"

Rahman, Moqsadur, Krish Piryani, Aaron Sanchez, et al. Retrieval Augmented Generation for Robust Cyber Defense. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2474934.

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Sadat, Mohammad Ahnaf. CoralAI: A Retrieval-Augmented Generation Model for Coral-Related Queries. Iowa State University, 2024. https://doi.org/10.31274/cc-20250502-71.

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