Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.04.19.537463v1?rss=1
Authors: Chen, Q., Sun, H., Liu, H., Jiang, Y., Ran, T., Jin, X., Xiao, X., Lin, Z., Niu, Z., Chen, H.
Abstract:
In recent years, the development of natural language process (NLP) technologies and deep learning hardware has led to significant improvement in large language models(LLMs). The ChatGPT, the state-of-the-art LLM built on GPT-3.5, shows excellent capabilities in general language understanding and reasoning. Researchers also tested the GPTs on a variety of NLP related tasks and benchmarks and got excellent results. To evaluate the performance of ChatGPT on biomedical related tasks, this paper presents a comprehensive benchmark study on the use of ChatGPT for biomedical corpus, including article abstracts, clinical trials description, biomedical questions and so on. Through a series of experiments, we demonstrated the effectiveness and versatility of ChatGPT in biomedical text understanding, reasoning and generation.
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