Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study
Abstract
The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging. This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians' queries on emerging issues associated with health-related topics, using user-generated medical information on social media. We proposed a two-layer RAG framework for query-focused answer generation and evaluated a proof of concept for the framework in the context of query-focused summary generation from social media forums, focusing on emerging drug-related information. Our modular framework generates individual summaries followed by an aggregated summary to answer medical queries from large amounts of user-generated social media data in an efficient manner. We compared the performance of a quantized large language model (Nous-Hermes-2-7B-DPO), deployable in low-resource settings, with GPT-4. For this proof-of-concept study, we used user-generated data from Reddit to answer clinicians' questions on the use of xylazine and ketamine. Our framework achieves comparable median scores in terms of relevance, length, hallucination, coverage, and coherence when evaluated using GPT-4 and Nous-Hermes-2-7B-DPO, evaluated for 20 queries with 76 samples. There was no statistically significant difference between the two for coverage, coherence, relevance, length, and hallucination. A statistically significant difference was noted for the Coleman-Liau Index. Our RAG framework can effectively answer medical questions about targeted topics and can be deployed in resource-constrained settings.
Cite
@article{arxiv.2405.19519,
title = {Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study},
author = {Sudeshna Das and Yao Ge and Yuting Guo and Swati Rajwal and JaMor Hairston and Jeanne Powell and Drew Walker and Snigdha Peddireddy and Sahithi Lakamana and Selen Bozkurt and Matthew Reyna and Reza Sameni and Yunyu Xiao and Sangmi Kim and Rasheeta Chandler and Natalie Hernandez and Danielle Mowery and Rachel Wightman and Jennifer Love and Anthony Spadaro and Jeanmarie Perrone and Abeed Sarker},
journal= {arXiv preprint arXiv:2405.19519},
year = {2025}
}
Comments
Published in JMIR: https://www.jmir.org/2025/1/e66220