[ACL 2024 Findings] MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning https://arxiv.org/abs/2311.10537

A large-scale (194k), Multiple-Choice Question Answering (MCQA) dataset designed to address realworld medical entrance exam questions.

A novel medical large language model family with 13/70B parameters, which have SOTA performances on various medical tasks

Medical Question-Answering datasets prepared for the TREC 2017 LiveQA challenge (Medical Task)

[Paper][WWW2025] OntoTune: Ontology-Driven Self-training for Aligning Large Language Models

The First Generate-then-Read Framework for Multiple-Choice Question Answering in Medicine

The use of ChatGPT in the healthcare sector

A Columbia University capstone project focused on mitigating hallucinations in Medical Question Answering systems using Retrieval-Augmented Generation (RAG), ElasticSearch, and LLM-based validation.

This repo contains the code for the paper Does Domain-Specific Retrieval Augmented Generation Help LLMs Answer Consumer Health Questions?

Persian Question Answering Dataset

Medquery is designed to help healthcare professionals access accurate, up-to-date medical information quickly and efficiently, and receive patient-specific, evidence-based guidance that supports informed decision-making.

In this we implement Question Answering task on Medical domain using DeepSeek-R1 Model