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Papers on “retrieval augmented generation large language models”

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1. Adaptive Control of Retrieval-Augmented Generation for Large Language Models Through Reflective Tags

   Chengyuan Yao, Satoshi Fujita · 2024 · Electronics · 15 cites

   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 parallel and applying the chain-of-thought (CoT) technique for step-by-step generation. The model sel

2. Information retrieval from textual data: Harnessing large language models, retrieval augmented generation and prompt engineering

   Asen Hikov, Laura Murphy · 2024 · Journal of AI, Robotics & Workplace Automation · 14 cites

   This paper describes how recent advancements in the field of Generative AI (GenAI), and more specifically large language models (LLMs), are incorporated into a practical application that solves the widespread and relevant business problem of information retrieval from textual data in PDF format: searching through legal texts, financial reports, research articles and so on. Marketing research, for example, often requires reading through hundreds of pages of financial reports to extract key information for research on competitors, partners, markets and prospective clients. It is a manual, error-prone and time-consuming task for marketers, where until recently there was little scope for automat

3. Optimierte Interaktion mit Large Language Models

   Anna Fink, Alexander Rau, Elmar Kotter, et al. · 2025 · Die Radiologie · 6 cites

4. Nursing Retrieval-Augmented Generation: Retrieval augmented generation for nursing question answering with large language models

   Liping Xiong, Qiqiao Zeng, Weixiang Luo, et al. · 2025 · International Journal of Nursing Sciences · 5 cites

5. The Role of Retrieval-Augmented Generation in Improving Factual Accuracy for Medical Large Language Models

   Eason Ni · 2026 · Scholarly Review Journal

   Large Language Models (LLMs) that rely solely on parametric memory learned through training have demonstrated strong performance in biomedical question-answering, but their tendency to hallucinate facts and the difficulty of adjusting and adding to the learned knowledge limit their usefulness in clinical settings. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by adding a non-parametric source of memory and grounding LLM outputs to reputable external sources. This paper aims to survey the evolution of RAG methodologies and highlight the most recent developments and shifts in paradigms like Agentic RAG. We highlight current state-of-the-art biomedical RAG systems, th

6. A Framework for Adaptive Knowledge-Augmented Mizo Large Language Models Using Retrieval-Augmented Generation and Continual Learning

   Vanlalropuia Ralte, Abhisake Sinha - · 2026 · International Journal For Multidisciplinary Research

   The deployment of Large Language Models (LLMs) for low-resource languages is challenging due to the lack of linguistic resources, sparse digital content and the absence of structured knowledge bases. In this paper, we present an adaptive knowledge-augmented framework for Mizo Large Language Models by combining Retrieval-Augmented Generation (RAG) with continual learning. This methodology harnesses semantic retrieval with dense embeddings and FAISS indexing, adaptive evidence re-ranking, parameter-efficient fine-tuning, and incremental knowledge updating to enhance factual accuracy and decrease hallucinations. Experimental evaluation shows better retrieval performance, greater text creation q

7. Intelligent automation of economic processes based on retrieval-augmented generation and large language models

   Serhii Arefiev, Serhii Hildi · 2026 · Ukrainian Journal of Applied Economics and Technology

   The article is devoted to the theoretical and methodological substantiation of the concept of intelligent automation of economic processes based on the integration of Retrieval-Augmented Generation (RAG), Large Language Models (LLM), Prompt Engineering, and Automatic Engineering technologies. The modern digital economy is moving from technical automation to cognitive automation, in which self-learning systems are emerging that can adapt to environmental changes, analyze the results of their own activities, and generate new economic solutions. RAG acts as a cognitive intermediary between generation and data retrieval, ensuring the factual reliability of analytical results. LLMs provide semant

8. Quantum-Enhanced Retrieval-Augmented Generation for Hallucination Reduction in Large Language Models

   Praveenkumar Seepana · 2026 · International Journal of Computational Science and Engineering Research

   Although the performance of LLMs on a wide variety of natural language processing problems demonstrates remarkable ability, hallucinated responses are introduced as one of the key weaknesses of LLMs, especially in knowledge-intensive applications, where fidelity to facts is paramount. While effective in reducing hallucinations, the context retrieved during the RAG operations is still very often suboptimal with respect to the external documents used in the retrieval stage, and being based on cosine similarity and nearest-neighbour search, these models typically do not return optimal context to support factual generation. The authors propose a five-stage solution, called Quantum-Enhanced Retri