Thematische Bibliographien / Retrieval Augmented Generation (RAG)

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Auswahl der wissenschaftlichen Literatur zum Thema „Retrieval Augmented Generation (RAG)“

Autor: Grafiati

Veröffentlicht am 17. Mai 2025

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Zeitschriftenartikel zum Thema "Retrieval Augmented Generation (RAG)"

Mishra, Ankit, und Aniket Gupta. „Retrieval Augmented Generation (RAG) Model“. International Journal of Research Publication and Reviews 6, Nr. 6 (Januar 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, Nr. 1 (24.04.2025): 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) with a sequence-to-sequence generator (GPT-3.5-turbo), jointly optimized in an end-to-end manner to retrieve and utilize relevant knowledge effectively. This paper evaluates RAG on MS MARCO, demonstrating its superiority over state-of-the-art purely generative models and traditional retrieval-based systems. Experimental results show that RAG achieves significant improvements in factual accuracy, relevance, and interpretability, as measured by metrics such as term frequencyinverse document frequency, bidirectional encoder representation from transformer Score, and Q-Bilingual Evaluation Understudy-1.

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Long, Xinwei, Zhiyuan Ma, Ermo Hua, Kaiyan Zhang, Biqing Qi und Bowen Zhou. „Retrieval-Augmented Visual Question Answering via Built-in Autoregressive Search Engines“. Proceedings of the AAAI Conference on Artificial Intelligence 39, Nr. 23 (11.04.2025): 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 only helps retrieve documents from the knowledge base by producing identifier for each document, but it also answers visual questions based on the retrieved documents. Furthermore, we also propose a reinforced retrieval calibration module from relevance feedback to improve retrieval performance and align with the preferences for accurate answer generation. Extensive experiments on two representative OKVQA and A-OKVQA datasets demonstrate significant improvements ranging from 2.9% to 9.6% across all evaluation metrics when compared to strong baselines.

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Han, Binglan, Teo Susnjak und Anuradha Mathrani. „Automating Systematic Literature Reviews with Retrieval-Augmented Generation: A Comprehensive Overview“. Applied Sciences 14, Nr. 19 (09.10.2024): 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 precision of real-time information retrieval. We review in detail the three key processes of the RAG framework—retrieval, augmentation, and generation. We then discuss applications of RAG-based LLMs to SLR automation and highlight future research topics, including integration of domain-specific LLMs, multimodal data processing and generation, and utilization of multiple retrieval sources. We propose a framework of RAG-based LLMs for automating SRLs, which covers four stages of SLR process: literature search, literature screening, data extraction, and information synthesis. Future research aims to optimize the interaction between LLM selection, training strategies, RAG techniques, and prompt engineering to implement the proposed framework, with particular emphasis on the retrieval of information from individual scientific papers and the integration of these data to produce outputs addressing various aspects such as current status, existing gaps, and emerging trends.

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Choi, Yein, Sungwoo Kim, Yipene Cedric Francois Bassole und Yunsick Sung. „Enhanced Retrieval-Augmented Generation Using Low-Rank Adaptation“. Applied Sciences 15, Nr. 8 (17.04.2025): 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 embedding generation using a LoRA-enhanced BERT-based uncased model and an optimized retrieval strategy that combines maximal marginal similarity score thresholding with contextual compression retrieval. The proposed system enhances and achieves improved retrieval accuracy while reducing processing overhead. Leveraging road traffic-related regulatory datasets, the LoRA-enhanced model demonstrated remarkable performance gains over traditional RAG methods. Specifically, our model reduced the number of trainable parameters by 13.6% and lowered computational costs by 18.7%. Performance evaluations using BLEU, CIDEr, and SPICE scores revealed a 4.36% increase in BLEU-4, a 6.83% improvement in CIDEr, and a 5.46% improved in SPICE, confirming greater structural accuracy in regulatory text generation. Additionally, our method achieved an 8.5% improvement in retrieval accuracy across key metrics, outperforming baseline RAG systems. These contributions pave the way for more efficient and reliable traffic regulation processing, enabling better decision making in autonomous systems.

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Grabuloski, Marko, Aleksandar Karadimce und Anis Sefidanoski. „Enhancing Language Models with Retrieval-Augmented Generation A Comparative Study on Performance“. WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 22 (02.04.2025): 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, question-answering, conversational) and retrieval-augmentation strategies (sentence-level, paragraph-level) on the performance of RAG systems. We conducted experiments using various open-source LLMs and a custom-built RAG system to assess the effectiveness of different approaches. The findings indicate that RAG systems can significantly enhance the performance of LLMs, especially for complex questions that require access to diverse information sources. T5 conversational models, in particular, demonstrate strong performance in synthesis-based tasks, effectively combining information from multiple retrieved documents. However, causal and question-answering models may struggle with complex reasoning and synthesis, even with RAG augmentation.

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Chen, Jiawei, Hongyu Lin, Xianpei Han und Le Sun. „Benchmarking Large Language Models in Retrieval-Augmented Generation“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 16 (24.03.2024): 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, including noise robustness, negative rejection, information integration, and counterfactual robustness. To this end, we establish Retrieval-Augmented Generation Benchmark (RGB), a new corpus for RAG evaluation in both English and Chinese. RGB divides the instances within the benchmark into 4 separate testbeds based on the aforementioned fundamental abilities required to resolve the case. Then we evaluate 6 representative LLMs on RGB to diagnose the challenges of current LLMs when applying RAG. Evaluation reveals that while LLMs exhibit a certain degree of noise robustness, they still struggle significantly in terms of negative rejection, information integration, and dealing with false information. The aforementioned assessment outcomes indicate that there is still a considerable journey ahead to effectively apply RAG to LLMs.

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Dong, Guanting, Xiaoshuai Song, Yutao Zhu, Runqi Qiao, Zhicheng Dou und Ji-Rong Wen. „Toward Verifiable Instruction-Following Alignment for Retrieval Augmented Generation“. Proceedings of the AAAI Conference on Artificial Intelligence 39, Nr. 22 (11.04.2025): 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 auto-evaluation for RAG systems, we introduce FollowRAG Benchmark, which includes approximately 3K test samples, covering 22 categories of general instruction constraints and four knowledge-intensive QA datasets. Due to its robust pipeline design, FollowRAG can seamlessly integrate with different RAG benchmarks. Using FollowRAG and eight widely-used IF and foundational abilities benchmarks for LLMs, we demonstrate that VIF-RAG markedly enhances LLM performance across a broad range of general instruction constraints while effectively leveraging its capabilities in RAG scenarios. Further analysis offers practical insights for achieving IF alignment in RAG systems.

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Shaji, Edwin Alex, Jerishab M. Jerishab M, Leya Thomas, M. Viraj Prabhu und Asst Prof Chinchu M Pillai. „Survey on Speech Recognition and Retrieval-Augmented Generation“. International Journal of Advances in Engineering and Management 06, Nr. 12 (Dezember 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 categorizing the papers into themes, this survey highlights key methodologies, compares their performance, and identifies future directions for improving ASR and RAG technologies in handling real-world challenges.

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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, Nr. 1 (30.04.2025): 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 encompassing query processing, information retrieval, contextualization, response generation, and delivery. This hybrid architecture yields substantial improvements in factual accuracy, knowledge recency, system transparency, and operational efficiency. The article further examines critical implementation considerations including vector database selection, embedding model optimization, document chunking strategies, retrieval algorithm configuration, and prompt engineering techniques. Looking toward future developments, the article highlights promising directions including multi-modal capabilities, hybrid retrieval methodologies, adaptive retrieval systems, and enterprise knowledge integration. It demonstrates how RAG represents a significant advancement in creating more intelligent, reliable, and context-aware AI conversational systems.

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Dissertationen zum Thema "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 pour limiter le risque d'hallucinations est la génération augmentée par récupération (RAG), qui permet d'injecter des connaissances lors de la génération de texte. Ces connaissances peuvent être extraites de graphes de connaissances (KG), qui sont des représentations structurées et accessibles pour les systèmes informatiques. Ainsi, nous explorons les architectures de KG-RAG pour construire des agents conversationnels de confiance. Nous démontrons l'intérêt de notre approche pour un cas d'usage réel de support citoyen·ne en construisant un agent conversationnel traitant les mesures autour du handicap dans des villes françaises. Nous présentons d'abord un historique des agents conversationnels, en introduisant les méthodes mises en œuvre au fil des années et les techniques d'évaluation. Nous définissons ensuite les KG et les ontologies, et explorons les techniques de construction et d'évaluation. Ne trouvant pas de KG directement exploitable, notre première contribution introduit OLAF : Ontology Learning Applied Framework. Ce système modulaire est conçu pour une construction automatisée et reproductible de KG à partir de textes non structurés. OLAF intègre des techniques linguistiques, statistiques et basées sur des LLM pour générer des ontologies minimales viables sur des domaines spécifiques. Appliqué à des ensembles de données réels, OLAF démontre des performances robustes grâce à des évaluations basées sur des ontologies de référence et des questions de compétence spécifiques à une tâche. Nous détaillons le processus de construction d'un KG sur la thématique du handicap dans une ville française. Nous proposons ensuite une architecture pour les systèmes de KG-RAG afin d'améliorer la recherche d'information en alignant les requêtes des utilisateur·rice·s avec les structures des graphes via la liaison d'entités, les patrons de requêtes et les méthodes de récupération basées sur les LLM. Nous démontrons l'intérêt de notre architecture sur différents cas d'utilisation, que nous évaluons selon des critères tels que la performance, les préférences humaines et l'impact environnemental. Bien que les préférences des utilisateur·rice·s avantagent l'architecture de Text-RAG, l'impact environnemental réduit de l'architecture de KG-RAG souligne son potentiel pour des pratiques d'IA durables. Enfin, nous identifions comme élément clé de l'architecture la partie concernant la recherche d'information. Nous abordons donc cette tâche dans notre architecture en explorant les techniques de vectorisation dans divers contextes, c'est-à-dire en améliorant la liaison d'entités, la recherche des données contextuelles et en fournissant un système de cache. Nous proposons également des mécanismes pour gérer les conversations multi-tours. Ce travail établit un cadre complet pour les systèmes de KG-RAG, combinant la sémantique des KG avec les capacités génératives des LLM pour construire des agents conversationnels précis, spécialisés et durables. Les contributions incluent OLAF pour une construction automatisée de KG, un pipeline de KG-RAG robuste, et des améliorations basées sur des représentations vectorielles pour la précision de la recherche d'information et la qualité des interactions. En répondant aux défis industriels des agents conversationnels, ces travaux posent les bases du déploiement de systèmes de KG-RAG dans des domaines spécialisés et variés
Conversational agents have become widespread in recent years. Today, they have transcended their initial purpose of simulating a conversation with a computer program and are now valuable tools for accessing information and carrying out various tasks, from customer service to personal assistance. With the rise of text-generative models and Large Language Models (LLMs), the capabilities of conversational agents have increased tenfold. However, they are now subject to hallucinations, producing false information. A popular technique to limit the risk of hallucinations is Retrieval Augmented Generation (RAG), which injects knowledge into a text generation process. Such injected knowledge can be drawn from Knowledge Graphs (KGs), which are structured machine-readable knowledge representations. Therefore, we explore Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) to build trusted conversational agents. We demonstrate our approach on a real-world use case for citizen support by building conversational agents for disability management in cities. We first present a history of conversational agents, introducing the approaches implemented over the years and the evaluation techniques. We then define KGs and ontologies, and explore construction and evaluation techniques. As we could not find a directly exploitable KG, our first contribution introduces the Ontology Learning Applied Framework (OLAF). This modular system is built for automated and repeatable KG construction from unstructured text. OLAF integrates linguistic, statistical, and LLM-based techniques to generate Minimum Viable Ontologies for specific domains. Applied to real-world datasets, OLAF demonstrates robust performance through gold-standard evaluations and task-specific Competency Questions. We detail the construction process for a KG about disability management in a French city. We then propose an architecture for KG-RAG systems to enhance information retrieval by aligning user queries with KG structures through entity linking, graph queries, and LLM-based retrieval approaches. We demonstrate our architecture on different use cases, which we evaluate using criteria such as performance, human preference, and environmental impact. While user preferences advantage Text-RAG, KG-RAG's reduced computational footprint underscores its potential for sustainable AI practices. Finally, we identify the critical part of the architecture as the retriever. Therefore, we tackle the retrieval task in our architecture by exploring embeddings in various contexts, i.e. improving EL, retrieval, and providing a caching system. We also propose mechanisms for handling multi-turn conversations. This work establishes a comprehensive framework for KG-RAG systems, combining the semantic depth of KGs with the generative capabilities of LLMs to deliver accurate, contextual, and sustainable conversational agents. Contributions include OLAF for scalable KG construction, a robust KG-RAG pipeline, and embedding-based enhancements for retrieval and interaction quality. By addressing conversational agents' industrial challenges, such as scalability, retrieval precision, and conversational coherence, this research lays the foundation for deploying KG-RAG systems in diverse and specialised domains

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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 model is composed of a retriever and a generator both of which are based on Transformer and it has been trained firstly in a self-supervised way on the library of Gruppo Maggioli company, and then in a supervised way on a novel Italian question answering dataset build on purpose. Once the user has provided an input, the model automatically retrieves possibly relevant documents from the knowledge base and use them to condition the generation of an appropriate answer.

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Bücher zum Thema "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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Buchteile zum Thema "Retrieval Augmented Generation (RAG)"

Parasuraman, Banu. „Spring AI and RAG (Retrieval-Augmented Generation)“. In Mastering Spring AI, 115–79. Berkeley, CA: Apress, 2024. https://doi.org/10.1007/979-8-8688-1001-5_4.

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Shan, Richard, und Tony Shan. „Retrieval-Augmented Generation Architecture Framework: Harnessing the Power of RAG“. In Lecture Notes in Computer Science, 88–104. Cham: 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 und Shivam Tiwadi. „Real-time crisis response optimization with Retrieval Augmented Generation (RAG)“. In Intelligent Computing and Communication Techniques, 86–91. London: CRC Press, 2025. https://doi.org/10.1201/9781003530190-14.

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Babaei Giglou, Hamed, Tilahun Abedissa Taffa, Rana Abdullah, Aida Usmanova, Ricardo Usbeck, Jennifer D’Souza und Sören Auer. „Scholarly Question Answering Using Large Language Models in the NFDI4DataScience Gateway“. In Lecture Notes in Computer Science, 3–18. Cham: 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 the scholarly QA system is demonstrated through experimental analysis.

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Holvoet, Laura, Michael van Bekkum und Aijse de Vries. „An Approach to Automated Instruction Generation with Grounding Using LLMs and RAG“. In Lecture Notes in Mechanical Engineering, 224–33. Cham: 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. Recently, Generative AI models and Large Language Models (LLMs) have taken center stage with their abilities to interact fluently with humans, both in understanding user questions/statements and in convincingly producing natural language texts. These models however suffer from several problems, including hallucinations where unsubstantiated content is presented as facts and lack of domain-specific data about products and procedures. For instruction generation however, we need verifiably correct statements about the task at hand. To tackle both problems, we have created a pipeline that combines the generative abilities of LLMs with explicit domain-specific data. We deploy a variant of Retrieval Augmented Generation (RAG) and incorporate an ontology that augments the instructions with additional information (policies, warnings, tools). Our results show an increase in correctness of output.

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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, 259–313. Berkeley, CA: Apress, 2024. https://doi.org/10.1007/979-8-8688-0882-1_7.

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Nazary, Fatemeh, Yashar Deldjoo und Tommaso di Noia. „Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender Systems“. In Lecture Notes in Computer Science, 239–51. Cham: 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, Eric Zhang, Ryan Nguyen, Daniel Campos, Nick Craswell und Jimmy Lin. „Ragnarök: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track“. In Lecture Notes in Computer Science, 132–48. Cham: 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, Kyle Martin, Stewart Massie, Ikechukwu Nkisi-Orji, Ruvan Weerasinghe, Anne Liret und Bruno Fleisch. „CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering“. In Case-Based Reasoning Research and Development, 445–60. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63646-2_29.

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Otto, Wolfgang, Sharmila Upadhyaya und Stefan Dietze. „Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models“. In Lecture Notes in Computer Science, 289–306. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65794-8_21.

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AbstractThis paper describes our participation in the Shared Task on Software Mentions Disambiguation (SOMD), with a focus on improving relation extraction in scholarly texts through generative Large Language Models (LLMs) using single-choice question-answering. The methodology prioritises the use of in-context learning capabilities of LLMs to extract software-related entities and their descriptive attributes, such as distributive information. Our approach uses Retrieval-Augmented Generation (RAG) techniques and LLMs for Named Entity Recognition (NER) and Attributive NER to identify relationships between extracted software entities, providing a structured solution for analysing software citations in academic literature. The paper provides a detailed description of our approach, demonstrating how using LLMs in a single-choice QA paradigm can greatly enhance IE methodologies. Our participation in the SOMD shared task highlights the importance of precise software citation practices and showcases our system’s ability to overcome the challenges of disambiguating and extracting relationships between software mentions. This sets the groundwork for future research and development in this field.

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Konferenzberichte zum Thema "Retrieval Augmented Generation (RAG)"

Tural, Büşra, Zeynep Örpek und Zeynep Destan. „Retrieval-Augmented Generation (RAG) and LLM Integration“. In 2024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS), 1–5. IEEE, 2024. https://doi.org/10.1109/isas64331.2024.10845308.

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Rappazzo, Brendan Hogan, Yingheng Wang, Aaron Ferber und Carla Gomes. „GEM-RAG: Graphical Eigen Memories for Retrieval Augmented Generation“. In 2024 International Conference on Machine Learning and Applications (ICMLA), 1259–64. IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00196.

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Rani, Maneeha, Bhupesh Kumar Mishra, Dhavalkumar Thakker und 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), 1–6. 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 und 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), 1–10. IEEE, 2024. http://dx.doi.org/10.1109/bigdataservice62917.2024.00008.

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Danuarta, Leo, Viny Christanti Mawardi und Viciano Lee. „Retrieval-Augmented Generation (RAG) Large Language Model For Educational Chatbot“. In 2024 Ninth International Conference on Informatics and Computing (ICIC), 1–6. IEEE, 2024. https://doi.org/10.1109/icic64337.2024.10957676.

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Sawarkar, Kunal, Abhilasha Mangal und 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), 155–61. IEEE, 2024. http://dx.doi.org/10.1109/mipr62202.2024.00031.

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Agrawal, Garima, Tharindu Kumarage, Zeyad Alghamdi und 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), 607–11. IEEE, 2024. https://doi.org/10.1109/fllm63129.2024.10852457.

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Cai, Yucheng, Si Chen, Yuxuan Wu, Yi Huang, Junlan Feng und Zhijian Ou. „The 2nd Futuredial Challenge: Dialog Systems With Retrieval Augmented Generation (Futuredial-RAG)“. In 2024 IEEE Spoken Language Technology Workshop (SLT), 1091–98. IEEE, 2024. https://doi.org/10.1109/slt61566.2024.10832299.

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Xiao, Wei, Yu Liu, XiangLong Li, Feng Gao und JinGuang Gu. „TKG-RAG: A Retrieval-Augmented Generation Framework with Text-chunk Knowledge Graph“. In 2024 25th International Arab Conference on Information Technology (ACIT), 1–9. IEEE, 2024. https://doi.org/10.1109/acit62805.2024.10877117.

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Song, Juntong, Xingguang Wang, Juno Zhu, Yuanhao Wu, Xuxin Cheng, Randy Zhong und Cheng Niu. „RAG-HAT: A Hallucination-Aware Tuning Pipeline for LLM in Retrieval-Augmented Generation“. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, 1548–58. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-industry.113.

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Berichte der Organisationen zum Thema "Retrieval Augmented Generation (RAG)"

Rahman, Moqsadur, Krish Piryani, Aaron Sanchez, Sai Munikoti, Luis De La Torre, Maxwell Levin, Monika Akbar, Mahmud Hossain, Monowar Hasan und Mahantesh Halappanavar. Retrieval Augmented Generation for Robust Cyber Defense. Office of Scientific and Technical Information (OSTI), September 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--Ames: Iowa State University, Dezember 2024. https://doi.org/10.31274/cc-20250502-71.

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