Résultats du processus de synthèse de l'article arXiv : 2304.14454v1

Résumé : 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.