# MedLM: Exploring Language Models for Medical Question Answering Systems

Source: [MedLM: Exploring Language Models for Medical Question Answering Systems](<https://www.sota2.com/research/paper/68fa72621e209131cea56193>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:25:11.364Z

Authors: Niraj Yagnik, Jay Jhaveri, Vivek Sharma, Gabriel Pila

Published: 2024-01-21T03:37:47.000Z

## 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 fill this gap by comparing the performance of general and medical-specific distilled LMs for medical Q&A. We aim to evaluate the effectiveness of fine-tuning domain-specific LMs and compare the performance of different families of Language Models. The study will address critical questions about these models' reliability, comparative performance, and effectiveness in the context of medical Q&A. The findings will provide valuable insights into the suitability of different LMs for specific applications in the medical domain.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2401.11389>)

[PDF](<https://arxiv.org/pdf/2401.11389.pdf>)