{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:49:24Z","timestamp":1747216164605,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643684567"},{"type":"electronic","value":"9781643684574"}],"license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,25]]},"abstract":"<jats:p>List-type questions, which can have a varying number of answers, are more common in the health domain where people seek for health-related information from a passage or passages. An example of this type of question answering task is to find COVID-19 symptoms from a Twitter post. However, due to the lack of annotated instances for supervised learning, automatic identification of COVID-19 symptoms from Twitter posts is challenging. We investigated detection of symptom mentions in Twitter posts using GPT-3, a pre-trained large language model, along with few-shot learning. Our results of 5-shot and 10-shot learning on a corpus of 655 annotated tweets demonstrate that few-shot learning with pre-trained large language model is a promising approach to answering list-type questions with a minimal amount of effort of annotation.<\/jats:p>","DOI":"10.3233\/shti231041","type":"book-chapter","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:24:39Z","timestamp":1706178279000},"source":"Crossref","is-referenced-by-count":0,"title":["Answering List-Type Questions in Health Domain with Pretrained Large Language Model: A Case for COVID-19 Symptoms"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1565-3202","authenticated-orcid":false,"given":"Keyuan","family":"Jiang","sequence":"first","affiliation":[{"name":"Purdue University Northwest, Hammond, Indiana, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed M.","family":"Mujtaba","sequence":"additional","affiliation":[{"name":"Purdue University Northwest, Hammond, Indiana, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gordon R.","family":"Bernard","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, Tennessee, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2023 \u2014 The Future Is Accessible"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI231041","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:24:41Z","timestamp":1706178281000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI231041"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"ISBN":["9781643684567","9781643684574"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti231041","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}