bioRxiv (Cold Spring Harbor Laboratory) · 2023 · 22 citations · 19 references
ComputingComputer ScienceBiomedical Text MiningNatural Language ProcessingText AnalyticsClinical Natural Language ProcessingLanguage ModelingNatural Language GenerationQuestion AnsweringText GenerationAutomated Text GenerationBiomedical Question AnsweringClinical Question AnsweringClinical Text GenerationLanguage UnderstandingLarge Language Models
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 Chat-GPT in biomedical text understanding, reasoning and generation.
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang et al. · arXiv (Cornell University) · 2022 · 4.3K citations · Full text
Language Models are Few-Shot Learners
T. B. Brown, Benjamin Mann, Nick Ryder et al. · arXiv (Cornell University) · 2020 · 3K citations · Full text