HuatuoGPT, towards Taming Language Model to Be a Doctor
Hongbo Zhang Junying Chen Feng Jiang Fei Yu Zhihong Chen Jianquan Li Guiming Chen Xiangbo Wu Zhiyi Zhang Qingying Xiao Xiang Wan Benyou Wang Haizhou Li
In this paper, we present HuatuoGPT, a large language model (LLM) for medical
consultation. The core recipe of HuatuoGPT is to leverage both
\textit{distilled data from ChatGPT} and \textit{real-world data from doctors}
in the supervised fine-tuned stage. The responses of ChatGPT are usually
detailed, well-presented and informative while it cannot perform like a doctor
in many aspects, e.g. for integrative diagnosis. We argue that real-world data
from doctors would be complementary to distilled data in the sense the former
could tame a distilled language model to perform like doctors. To better
leverage the strengths of both data, we train a reward model to align the
language model with the merits that both data bring, following an RLAIF
(reinforced learning from AI feedback) fashion. To evaluate and benchmark the
models, we propose a comprehensive evaluation scheme (including automatic and
manual metrics). Experimental results demonstrate that HuatuoGPT achieves
state-of-the-art results in performing medical consultation among open-source
LLMs in GPT-4 evaluation, human evaluation, and medical benchmark datasets. It
is worth noting that by using additional real-world data and RLAIF, the
distilled language model (i.e., HuatuoGPT) outperforms its teacher model
ChatGPT in most cases. Our code, data, and models are publicly available at
\url{https://github.com/FreedomIntelligence/HuatuoGPT}. The online demo is
available at \url{https://www.HuatuoGPT.cn/}.