Me-LLaMA: Foundation Large Language Models for Medical Applications

Recent advancements in large language models (LLMs) such as ChatGPT and LLaMA
have hinted at their potential to revolutionize medical applications, yet
their application in clinical settings often reveals limitations due to a lack
of specialized training on medical-specific data. In response to this
challenge, this study introduces Me-LLaMA, a medical LLM family that includes
foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions -
Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction
tuning of LLaMA2 using large medical datasets. Our methodology leverages a
comprehensive domain-specific data suite, including a large-scale, continual
pre-training dataset with 129B tokens, an instruction tuning dataset with 214k
samples, and we proposed a new medical evaluation benchmark (MIBE) across six
critical medical tasks with 12 datasets. Our extensive evaluation using the
MIBE shows that Me-LLaMA models achieve overall better performance than
existing open-source medical LLMs in zero-shot, few-shot and supervised
learning abilities. With task-specific instruction tuning, Me-LLaMA models
outperform ChatGPT on 7 out of 8 datasets and GPT-4 on 5 out of 8 datasets. In
addition, we investigated the catastrophic forgetting problem, and our results
show that Me-LLaMA models outperform other open-source medical LLMs in
mitigating this issue. Me-LLaMA is one of the largest open-source medical
foundation LLMs that use both biomedical and clinical data. It exhibits
superior performance across both general and medical tasks compared to other
open-source medical LLMs, rendering it an attractive choice for medical AI
applications.