{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:03:18Z","timestamp":1742979798465,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031637711"},{"type":"electronic","value":"9783031637728"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-63772-8_29","type":"book-chapter","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T06:03:07Z","timestamp":1719468187000},"page":"325-339","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Large Language Models for\u00a0Binary Health-Related Question Answering: A\u00a0Zero- and Few-Shot Evaluation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6560-9832","authenticated-orcid":false,"given":"Marcos","family":"Fern\u00e1ndez-Pichel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8823-7501","authenticated-orcid":false,"given":"David E.","family":"Losada","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9505-6493","authenticated-orcid":false,"given":"Juan C.","family":"Pichel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.resuscitation.2023.109729","volume":"185","author":"C Ahn","year":"2023","unstructured":"Ahn, C.: Exploring ChatGPT for information of cardiopulmonary resuscitation. Resuscitation 185, 109729 (2023)","journal-title":"Resuscitation"},{"key":"29_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10439-023-03171-8","volume":"51","author":"SS Biswas","year":"2023","unstructured":"Biswas, S.S.: Potential use of chat GPT in global warming. Ann. Biomed. Eng. 51, 1\u20132 (2023)","journal-title":"Ann. Biomed. Eng."},{"key":"29_CR3","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"29_CR4","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1016\/j.fertnstert.2023.05.151","volume":"120","author":"J Chervenak","year":"2023","unstructured":"Chervenak, J., Lieman, H., Blanco-Breindel, M., Jindal, S.: The promise and peril of using a large language model to obtain clinical information: ChatGPT performs strongly as a fertility counseling tool with limitations. Fertil. Steril. 120, 575\u2013583 (2023)","journal-title":"Fertil. Steril."},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Clarke, C., Maistro, M., Smucker, M.: Overview of the TREC 2021 health misinformation track. In: Proceedings of the Thirtieth Text REtrieval Conference, TREC (2021)","DOI":"10.6028\/NIST.SP.500-335.misinfo-overview"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Clarke, C., Maistro, M., Smucker, M., Zuccon, G.: Overview of the TREC 2020 health misinformation track. In: Proceedings of the Twenty-Nine Text REtrieval Conference, TREC, pp. 16\u201319 (2020)","DOI":"10.6028\/NIST.SP.1266.misinfo-overview"},{"key":"29_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"29_CR8","first-page":"1","volume":"32","author":"D Duong","year":"2023","unstructured":"Duong, D., Solomon, B.D.: Analysis of large-language model versus human performance for genetics questions. Eur. J. Hum. Genet. 32, 1\u20133 (2023)","journal-title":"Eur. J. Hum. Genet."},{"key":"29_CR9","unstructured":"Forbes: Introducing ChatGPT, November 2022. https:\/\/openai.com\/blog\/chatgpt. Acessed 4 Apr 2023"},{"key":"29_CR10","unstructured":"Fox, S.: Health topics: 80% of internet users look for health information online. Pew Internet & American Life Project (2011)"},{"key":"29_CR11","unstructured":"Golchin, S., Surdeanu, M.: Time travel in LLMs: tracing data contamination in large language models. arXiv preprint arXiv:2308.08493 (2023)"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Holmes, J., et\u00a0al.: Evaluating large language models on a highly-specialized topic, radiation oncology physics. arXiv preprint arXiv:2304.01938 (2023)","DOI":"10.3389\/fonc.2023.1219326"},{"key":"29_CR13","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1162\/tacl_a_00324","volume":"8","author":"Z Jiang","year":"2020","unstructured":"Jiang, Z., Xu, F.F., Araki, J., Neubig, G.: How can we know what language models know? Trans. Assoc. Comput. Linguist. 8, 423\u2013438 (2020)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Johnson, D., et\u00a0al.: Assessing the accuracy and reliability of AI-generated medical responses: an evaluation of the Chat-GPT model (2023)","DOI":"10.21203\/rs.3.rs-2566942\/v1"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Lachenbruch, P.A.: Mcnemar test. Wiley StatsRef: Statistics Reference Online (2014)","DOI":"10.1002\/9781118445112.stat04876"},{"issue":"4","key":"29_CR16","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2020)","journal-title":"Bioinformatics"},{"key":"29_CR17","unstructured":"Liang, P., et\u00a0al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022)"},{"key":"29_CR18","unstructured":"Lin, C.Y., Och, F.: Looking for a few good metrics: rouge and its evaluation. In: NTCIR Workshop (2004)"},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., Chen, W.: What makes good in-context examples for GPT-3? arXiv preprint arXiv:2101.06804 (2021)","DOI":"10.18653\/v1\/2022.deelio-1.10"},{"issue":"9","key":"29_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560815","volume":"55","author":"P Liu","year":"2023","unstructured":"Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55(9), 1\u201335 (2023)","journal-title":"ACM Comput. Surv."},{"key":"29_CR21","unstructured":"Longpre, S., et\u00a0al.: The flan collection: designing data and methods for effective instruction tuning. arXiv preprint arXiv:2301.13688 (2023)"},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Magar, I., Schwartz, R.: Data contamination: from memorization to exploitation. arXiv preprint arXiv:2203.08242 (2022)","DOI":"10.18653\/v1\/2022.acl-short.18"},{"key":"29_CR23","unstructured":"Nori, H., King, N., McKinney, S.M., Carignan, D., Horvitz, E.: Capabilities of GPT-4 on medical challenge problems. arXiv preprint arXiv:2303.13375 (2023)"},{"key":"29_CR24","unstructured":"OpenAI: GPT-4 technical report. arXiv:submit\/4812508 (2023)"},{"key":"29_CR25","first-page":"27730","volume":"35","author":"L Ouyang","year":"2022","unstructured":"Ouyang, L., et al.: Training language models to follow instructions with human feedback. Adv. Neural. Inf. Process. Syst. 35, 27730\u201327744 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Pogacar, F.A., Ghenai, A., Smucker, M.D., Clarke, C.L.: The positive and negative influence of search results on people\u2019s decisions about the efficacy of medical treatments. In: Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, pp. 209\u2013216 (2017)","DOI":"10.1145\/3121050.3121074"},{"key":"29_CR27","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/978-3-031-56066-8_9","volume-title":"Advances in Information Retrieval","author":"R Pradeep","year":"2024","unstructured":"Pradeep, R., Lin, J.: Towards automated end-to-end health misinformation free search with a large language model. In: Goharian, N., Tonellotto, N., He, Y., Lipani, A., McDonald, G., Macdonald, C., Ounis, I. (eds.) ECIR 2024. LNCS, vol. 14611, pp. 78\u201386. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-56066-8_9"},{"key":"29_CR28","doi-asserted-by":"crossref","unstructured":"Radfar, M., Mouchtaris, A., Kunzmann, S.: End-to-end neural transformer based spoken language understanding. arXiv preprint arXiv:2008.10984 (2020)","DOI":"10.21437\/Interspeech.2020-1963"},{"key":"29_CR29","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et\u00a0al.: Improving language understanding by generative pre-training (2018)"},{"issue":"8","key":"29_CR30","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., et al.: Language models are unsupervised multitask learners. OpenAI blog 1(8), 9 (2019)","journal-title":"OpenAI blog"},{"key":"29_CR31","first-page":"1","volume":"33","author":"JS Samaan","year":"2023","unstructured":"Samaan, J.S., et al.: Assessing the accuracy of responses by the language model ChatGPT to questions regarding bariatric surgery. Obes. Surg. 33, 1\u20137 (2023)","journal-title":"Obes. Surg."},{"key":"29_CR32","doi-asserted-by":"crossref","unstructured":"Sellam, T., Das, D., Parikh, A.P.: BLEURT: learning robust metrics for text generation. arXiv preprint arXiv:2004.04696 (2020)","DOI":"10.18653\/v1\/2020.acl-main.704"},{"key":"29_CR33","unstructured":"Sianz, O., Campos, J.A., Garc\u00eda-Ferrero, I., Etxaniz, J., Agirre, E.: Did ChatGPT cheat on your test? (2023). https:\/\/hitz-zentroa.github.io\/lm-contamination\/blog\/. Accessed 19 Jan 2024"},{"key":"29_CR34","doi-asserted-by":"crossref","unstructured":"Surameery, N.M.S., Shakor, M.Y.: Use chat GPT to solve programming bugs. Int. J. Inf. Technol. Comput. Eng. (IJITC) 3(01), 17\u201322 (2023). ISSN 2455-5290","DOI":"10.55529\/ijitc.31.17.22"},{"issue":"1","key":"29_CR35","doi-asserted-by":"publisher","DOI":"10.2196\/46599","volume":"9","author":"AJ Thirunavukarasu","year":"2023","unstructured":"Thirunavukarasu, A.J., et al.: Trialling a large language model (ChatGPT) in general practice with the applied knowledge test: observational study demonstrating opportunities and limitations in primary care. JMIR Med. Educ. 9(1), e46599 (2023)","journal-title":"JMIR Med. Educ."},{"key":"29_CR36","unstructured":"Vigdor, N.: Man fatally poisons himself while self-medicating for coronavirus, doctor says, March 2020. https:\/\/www.nytimes.com\/2020\/03\/24\/us\/chloroquine-poisoning-coronavirus.html. Accessed 9 June 2022"},{"key":"29_CR37","unstructured":"Yunxiang, L., Zihan, L., Kai, Z., Ruilong, D., You, Z.: ChatDoctor: a medical chat model fine-tuned on llama model using medical domain knowledge. arXiv preprint arXiv:2303.14070 (2023)"},{"key":"29_CR38","doi-asserted-by":"crossref","unstructured":"Zuccon, G., Koopman, B.: Dr ChatGPT, tell me what I want to hear: How prompt knowledge impacts health answer correctness. arXiv preprint arXiv:2302.13793 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.928"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63772-8_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T06:07:23Z","timestamp":1719468443000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63772-8_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031637711","9783031637728"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63772-8_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"28 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaga","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}