# HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge

**Authors:** Haochun Wang, Chi Liu, Nuwa Xi, Zewen Qiang, Sendong Zhao, Bing Qin, Ting Liu  
**arXiv:** [2304.06975](https://arxiv.org/abs/2304.06975)  
**Date:** 2023-04-14  
**Summary page:** https://www.summarizepaper.com/en/paper/2304.06975v1/

## Key points

- Introducing HuaTuo, a model to improve performance of Large Language Models (LLMs) in biomedical domain tasks
- HuaTuo addresses the challenge of LLMs lacking medical expertise in responses
- Proposes a supervised-fine-tuned LLaMA-based model utilizing generated Question-Answer (QA) instances
- Experimental results show HuaTuo generates responses with reliable medical knowledge
- Access to HuaTuo model provided on GitHub
- Sampling knowledge instances from a knowledge graph and generating instances based on specific knowledge using OpenAI API for training data
- Comparative analysis conducted with four baseline models showcasing superior performance of HuaTuo
- Incorporates Chinese medical knowledge into the model's training process

## Summary

The paper introduces HuaTuo, a model that aims to improve the performance of Large Language Models (LLMs) in biomedical domain tasks. HuaTuo addresses the challenge of LLMs lacking medical expertise in their responses by proposing a supervised-fine-tuned LLaMA-based model that utilizes generated Question-Answer (QA) instances. The experimental results demonstrate that HuaTuo generates responses with more reliable medical knowledge. The authors provide access to the HuaTuo model on GitHub and discuss the process of sampling knowledge instances from a knowledge graph and generating instances based on specific knowledge using the OpenAI API for training data. Additionally, comparative analysis is conducted with four baseline models to showcase the superior performance of HuaTuo. Overall, HuaTuo offers an improved solution for leveraging LLMs in biomedical domain tasks by incorporating Chinese medical knowledge into the model's training process.