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Papers on “retrieval augmented generation large language models”

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1. Retrieval-Augmented Generation for Large Language Models: A Survey

   Yunfan Gao, Yun Xiong, Xinyu Gao, et al. · 2023 · arXiv (Cornell University) · 874 cites

   Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases. This enhances the accuracy and credibility of the generation, particularly for knowledge-intensive tasks, and allows for continuous knowledge updates and integration of domain-specific information. RAG synergistically merges LLMs' intrinsic knowledge with the vast, dynamic repositories of external databases. This comprehensive review paper offers a detailed examination of the progression of

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2. Benchmarking Large Language Models in Retrieval-Augmented Generation

   Jiawei Chen, Hongyu Lin, Xianpei Han, et al. · 2024 · Proceedings of the AAAI Conference on Artificial Intelligence · 450 cites

   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

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3. SurgeryLLM: a retrieval-augmented generation large language model framework for surgical decision support and workflow enhancement

   Chin Siang Ong, Nicholas T. Obey, Yanan Zheng, et al. · 2024 · npj Digital Medicine · 47 cites

   SurgeryLLM, a large language model framework using Retrieval Augmented Generation demonstrably incorporated domain-specific knowledge from current evidence-based surgical guidelines when presented with patient-specific data. The successful incorporation of guideline-based information represents a substantial step toward enabling greater surgeon efficiency, improving patient safety, and optimizing surgical outcomes.

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4. 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

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5. 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 · 15 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

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6. The Development and Evaluation of a Retrieval-Augmented Generation Large Language Model Virtual Assistant for Postoperative Instructions

   Syed Ali Haider, Srinivasagam Prabha, Cesar Abraham Gomez Cabello, et al. · 2025 · Bioengineering · 9 cites

   BACKGROUND: During postoperative recovery, patients and their caregivers often lack crucial information, leading to numerous repetitive inquiries that burden healthcare providers. Traditional discharge materials, including paper handouts and patient portals, are often static, overwhelming, or underutilized, leading to patient overwhelm and contributing to unnecessary ER visits and overall healthcare overutilization. Conversational chatbots offer a solution, but Natural Language Processing (NLP) systems are often inflexible and limited in understanding, while powerful Large Language Models (LLMs) are prone to generating "hallucinations". OBJECTIVE: To combine the deterministic framework of tr

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7. 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 · 8 cites

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8. Evaluating Retrieval-Augmented Generation-Large Language Models for Infective Endocarditis Prophylaxis: Clinical Accuracy and Efficiency

   Paak Rewthamrongsris, Vivat Thongchotchat, Jirayu Burapacheep, et al. · 2025 · International Dental Journal · 7 cites

   INTRODUCTION AND AIMS: The use of large language models (LLMs) in healthcare is expanding. Retrieval-augmented generation (RAG) addresses key LLM limitations by grounding responses in domain-specific, up-to-date information. This study evaluated RAG-augmented LLMs for infective endocarditis (IE) prophylaxis in dental procedures, comparing their performance with non-RAG models assessed in our previous publication using the same question set. A pilot study also explored the utility of an LLM as a clinical decision support tool. METHODS: An established IE prophylaxis question set from previous research was used to ensure comparability. Ten LLMs integrated with RAG were tested using MiniLM L6 v2

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