Large Language Models (LLMs) have showcased remarkable capabilities in
natural language understanding in various domains. These models can usually
behave well on daily dialog, or question answering scenarios, however, in areas
that value precision, for example, in medical applications, they often exhibit
unsatisfactory performance due to a lack of domain-specific knowledge. In this
report, we introduce PMC-LLaMA, an open-source language model that is acquired
by fine-tuning an open-source language model on a total of 4.8 million
biomedical academic papers for further injecting medical knowledge, enhancing
its capability in medical domain. Our preliminary evaluations are conducted on
three biomedical QA datasets, including PubMedQA, MedMCQA, and USMLE, showing
that the our model after finetuning, i.e., PMC-LLaMA, demonstrates better
understanding of biomedical domain-specific concepts, thus achieving high
performance on QA benchmarks. The model and codes, along with an online demo,
are publicly available.

介绍了 PMC-LLaMA, 一种在 4.8 百万篇生物医学论文上 fine-tuning 得到的语言模型，用于注入医学知识，提高在医学领域的性能，经过初步试验后在生物医学数据集上表现出更好的理解生物医学特定概念，在 QA 基准上表现出高性能。