Summary of Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: a Case Study on Long-form Consumer Health Question Answering in Ophthalmology, by Aidan Gilson et al.

Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology

by Aidan Gilson, Xuguang Ai, Thilaka Arunachalam, Ziyou Chen, Ki Xiong Cheong, Amisha Dave, Cameron Duic, Mercy Kibe, Annette Kaminaka, Minali Prasad, Fares Siddig, Maxwell Singer, Wendy Wong, Qiao Jin, Tiarnan D.L. Keenan, Xia Hu, Emily Y. Chew, Zhiyong Lu, Hua Xu, Ron A. Adelman, Yih-Chung Tham, Qingyu Chen

First submitted to arxiv on: 20 Sep 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper explores the potential risks associated with using Large Language Models (LLMs) in medicine. Despite their capabilities, LLMs may generate responses lacking supporting evidence or based on hallucinated information. To address this issue, the authors developed a Retrieval Augment Generation (RAG) pipeline specifically designed for ophthalmology-related documents. The RAG pipeline retrieves relevant documents to augment LLMs during inference time, aiming to improve the accuracy and factuality of the responses. In a case study involving long-form consumer health questions, the authors compared the performance of LLMs with and without RAG, evaluating their responses based on evidence factuality, selection, ranking, attribution, answer accuracy, and completeness. The results show that LLMs without RAG provided over 500 references, but a significant portion of these (45.3%) were hallucinated, while others contained minor errors or were correct. In contrast, LLMs with RAG demonstrated improved accuracy (54.5% being correct) and reduced error rates. The RAG pipeline also showed improved evidence attribution, although it encountered challenges. The study highlights the risks of relying solely on LLMs in medical applications, emphasizing the need for reliable fact-checking mechanisms to ensure the accuracy and trustworthiness of responses. The authors’ findings demonstrate that RAG can be a valuable tool in mitigating these risks, but further research is necessary to fully address the challenges.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Large Language Models (LLMs) are powerful tools that can generate medical responses quickly. However, they may not always provide accurate or evidence-based answers. This study explores how to improve LLMs by using a technique called Retrieval Augment Generation (RAG). RAG helps LLMs find relevant information from the internet and use it to support their answers. The researchers tested LLMs with and without RAG on 100 medical questions, asking healthcare professionals to evaluate the responses. They found that LLMs without RAG often provided incorrect or irrelevant information. In contrast, LLMs with RAG produced more accurate answers and reduced errors. This study shows that RAG can be a useful tool for improving LLMs in medicine. However, there is still much to learn about how to make these models work better in the medical field.