{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:49Z","timestamp":1761176149506,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"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":[[2025,10,21]]},"abstract":"<jats:p>As large language models gain prominence, there is increasing concern about the potential biases they may perpetuate. While various biases have been studied, ageism in language models remains underexplored. According to the World Health Organization, ageism can significantly impact the physical and mental well-being of older adults, an impact that could grow as the global aging population increases. To address this research gap, we developed AgeismSet, a comprehensive Chinese dataset comprising 6,444 sentences, enhanced with neutral impression options to provide a balanced evaluation framework. Our study then used AgeismSet to investigate ageism in large language models across cognitive, affective, and behavioral dimensions, using AgeismSet to evaluate models such as GPT-4, GPT-3.5, GLM-3-Turbo, ERNIE Bot, Gemini Pro, and DeepSeek-V3. Our findings, quantified by the Ageism Score (AS), reveal that while some models perform well, there is considerable room for improvement in mitigating ageism. This work underscores the necessity for targeted interventions to ensure more equitable AI systems.<\/jats:p>","DOI":"10.3233\/faia250919","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:46:00Z","timestamp":1761126360000},"source":"Crossref","is-referenced-by-count":0,"title":["Measuring Ageism in Large Language Models"],"prefix":"10.3233","author":[{"given":"Shuo","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China"}]},{"given":"Jiaoyun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China"}]},{"given":"Yulong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China"}]},{"given":"Hongtu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Harvard Medical School, Boston, MA, United States"}]},{"given":"Ning","family":"An","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250919","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:46:00Z","timestamp":1761126360000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250919","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}