DoctorGPT: A Large Language Model with Chinese Medical Question-Answering Capabilities

Large Language Models (LLMs) have made incredible strides recently in understanding and reacting to user intents. However, these models typically excel in English and have not been specifically trained for medical applications, leading to suboptimal performance in responding to medical inquiries suc...

Full description

Saved in:
Bibliographic Details
Published in2023 International Conference on High Performance Big Data and Intelligent Systems (HDIS) pp. 186 - 193
Main Authors Li, Wenqiang, Yu, Lina, Wu, Min, Liu, Jingyi, Hao, Meilan, Li, Yanjie
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.12.2023
Subjects
artificial intelligence
Benchmark testing
Big Data
Biomedical equipment
Data models
Drugs
Encyclopedias
large language model
medical question-answering system
Medical services
Online AccessGet full text
DOI10.1109/HDIS60872.2023.10499472

Cover

More Information
Summary:Large Language Models (LLMs) have made incredible strides recently in understanding and reacting to user intents. However, these models typically excel in English and have not been specifically trained for medical applications, leading to suboptimal performance in responding to medical inquiries such as diagnostic queries and drug recommendations. In this paper, we propose DoctorGPT, a domain-specific large language model tailored for medical question-answering tasks. DoctorGPT leverages the open-source Baichuan2 as its foundational model, undergoes extensive pre-training on medical encyclopedic data to incorporate medical knowledge, and subsequently undergoes fine-tuning on a dataset consisting of two million medical instruction-dialogue pairs to enhance its question-answering capabilities. When compared to general-purpose large models, DoctorGPT demonstrates significant advantages in Chinese medical question-answerinz (O&A) tasks.
DOI:10.1109/HDIS60872.2023.10499472