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In the face of rapidly expanding online medical literature, automated systemsfor aggregating and summarizing information are becoming increasingly crucialfor healthcare professionals and patients. Large Language Models (LLMs), withtheir advanced generative capabilities, have shown promise in various NLPtasks, and their potential in the healthcare domain, particularly forClosed-Book Generative QnA, is significant. However, the performance of thesemodels in domain-specific tasks such as medical Q&A remains largely unexplored.This study aims to fill this gap by comparing the performance of general andmedical-specific distilled LMs for medical Q&A. We aim to evaluate theeffectiveness of fine-tuning domain-specific LMs and compare the performance ofdifferent families of Language Models. The study will address criticalquestions about these models' reliability, comparative performance, andeffectiveness in the context of medical Q&A. The findings will provide valuableinsights into the suitability of different LMs for specific applications in themedical domain.

MedLM: Exploring Language Models for Medical Question Answering Systems