MedLM: Exploring Language Models for Medical Question Answering Systems

arXiv:2401.11389v2 Announce Type: replace-cross
Abstract: In the face of rapidly expanding online medical literature, automated systems for aggregating and summarizing information are becoming increasingly crucial for healthcare professionals and patients. Large Language Models (LLMs), with their advanced generative capabilities, have shown promise in various NLP tasks, and their potential in the healthcare domain, particularly for Closed-Book Generative QnA, is significant. However, the performance of these models in domain-specific tasks such as medical Q&A remains largely unexplored. This study aims to …

abstract advanced arxiv automated capabilities cs.ai cs.cl cs.lg face generative healthcare information language language models large language large language models literature llms medical medlm nlp patients professionals question question answering summarizing systems tasks type