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Papers on “retrieval augmented generation large language models”

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1. Large language models encode clinical knowledge

   Karan Singhal, Shekoofeh Azizi, Tao Tu, et al. · 2023 · Nature · 3,813 cites

   Abstract Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. In addition, we evaluate Pathways Lan

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

   Yunfan Gao, Yun Xiong, Xinyu Gao, et al. · 2023 · arXiv (Cornell University) · 744 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   Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes.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

3. A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models

   Wenqi Fan, Yujuan Ding, Liangbo Ning, et al. · 2024 · 740 cites

   As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks. Particularly in the era of AI-Generated Content (AIGC), the powerful capacity of retrieval in providing additional knowledge enables RAG to assist existing generative AI in producing high-quality outputs. Recently, Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation, while still facing inherent limitations such as hallucinations and out-of-date internal knowledge. Given the powerful abilities of RAG in providing the latest and helpful auxiliary informa

4. 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 · 374 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

5. Retrieval augmented generation for large language models in healthcare: A systematic review

   Lameck Mbangula Amugongo, Pietro Mascheroni, Steven E. Brooks, et al. · 2025 · PLOS Digital Health · 219 cites

   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 evaluation frameworks are available. This review aims to bridge this gap by assessing RAG-based appro

6. Optimization of hepatological clinical guidelines interpretation by large language models: a retrieval augmented generation-based framework

   Simone Kresevic, Mauro Giuffrè, Miloš Ajčević, et al. · 2024 · npj Digital Medicine · 204 cites

   Large language models (LLMs) can potentially transform healthcare, particularly in providing the right information to the right provider at the right time in the hospital workflow. This study investigates the integration of LLMs into healthcare, specifically focusing on improving clinical decision support systems (CDSSs) through accurate interpretation of medical guidelines for chronic Hepatitis C Virus infection management. Utilizing OpenAI's GPT-4 Turbo model, we developed a customized LLM framework that incorporates retrieval augmented generation (RAG) and prompt engineering. Our framework involved guideline conversion into the best-structured format that can be efficiently processed by L

7. Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy

   Zhihong Shao, Yeyun Gong, Yelong Shen, et al. · 2023 · 180 cites

   Retrieval-augmented generation has raise extensive attention as it is promising to address the limitations of large language models including outdated knowledge and hallucinations.However, retrievers struggle to capture relevance, especially for queries with complex information needs.Recent work has proposed to improve relevance modeling by having large language models actively involved in retrieval, i.e., to guide retrieval with generation.In this paper, we show that strong performance can be achieved by a method we call ITER-RETGEN, which synergizes retrieval and generation in an iterative manner: a model's response to a task input shows what might be needed to finish the task, and thus ca

8. Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines

   Siru Liu, Allison B. McCoy, Adam Wright · 2025 · Journal of the American Medical Informatics Association · 166 cites

   OBJECTIVE: The objectives of this study are to synthesize findings from recent research of retrieval-augmented generation (RAG) and large language models (LLMs) in biomedicine and provide clinical development guidelines to improve effectiveness. MATERIALS AND METHODS: We conducted a systematic literature review and a meta-analysis. The report was created in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 analysis. Searches were performed in 3 databases (PubMed, Embase, PsycINFO) using terms related to "retrieval augmented generation" and "large language model," for articles published in 2023 and 2024. We selected studies that compared baseline LLM per