Detection of Day-Based Health Evidence with Pretrained Large Language Models: A Case of COVID-19 Symptoms in Social Media Posts.
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)(2023)
摘要
Gathering the information pertaining to health evidence occurring on particular days can help understand the progression of a disease over time, and such evidence as symptoms was widely shared by the social media users during the COVID-19 pandemic. Identifying this type of evidence is challenging. In this work, we investigated pretrained large language models on their ability to identify the day-based COVID-19 symptom mentions in Twitter posts. Our results on a corpus of 635 tweets show that without any supervision and optimization both GPT-3.5 and GPT 4 models achieved impressive performance, much better than Web-based ChatGPT and Google Bard. In addition, we explored and utilized GPT-4’s ability to determine the number of matches between a list of predicted symptom expressions and a list of annotated symptom expressions, reducing the effort of designing a sophisticated algorithm for finding the matches.
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关键词
health evidence detection,day-based evidence,large language models,COVID-19 symptoms,Twitter
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