Publication | Open Access
A Comprehensive Benchmark Study on Biomedical Text Generation and Mining with ChatGPT
21
Citations
19
References
2023
Year
Unknown Venue
EngineeringDeep Learning HardwareLarge Language ModelCorpus LinguisticsText MiningNatural Language ProcessingBiomedical Artificial IntelligenceData ScienceComprehensive Benchmark StudyComputational LinguisticsLanguage EngineeringBiostatisticsPublic HealthBiomedical Text MiningLanguage ModelsBiomedical QuestionsMachine TranslationClinical LanguageTranslational BioinformaticsNlp TaskGeneral Language UnderstandingBiomedical Text GenerationMedical Language ProcessingDeep LearningBioinformaticsText GenerationComputational BiologyText ProcessingLinguisticsHealth InformaticsLanguage Generation
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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