Exploring Biases Related to the Use of Large Language Models in a Multilingual Depression Corpus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploring Biases Related to the Use of Large Language Models in a Multilingual Depression Corpus Paula Andrea Pérez-Toro, Judith Dineley, Raquel Iniesta, Yuezhou Zhang, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5731243/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Recent advancements in Large Language Models (LLMs) present promising opportunities for applying these technologies to aid the detection and monitoring of Major Depression Disorder (MDD). However, demographic biases in LLMs may present challenges in the extraction of key information. This study evaluates commonly used LLMs in the speech health literature, across a cohort comprised of English, Spanish, and Dutch speakers with recurrent MDD to observe the effects of different demographic imbalances. Results indicate demographics indeed influence model performance. Gender showed variable impacts across models, with age presenting more pronounced differences. Model performance also varied across language. This study emphasizes the necessity of incorporating demographic-aware models in health-related analyses. It raises awareness of the biases that may affect their application in mental health and suggests further research on methods to mitigate these biases and enhance model generalization. Physical sciences/Mathematics and computing/Applied mathematics Physical sciences/Mathematics and computing/Computational science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 May, 2025 Reviews received at journal 25 Feb, 2025 Reviews received at journal 18 Feb, 2025 Reviewers agreed at journal 07 Feb, 2025 Reviewers agreed at journal 07 Feb, 2025 Reviewers agreed at journal 07 Feb, 2025 Reviewers invited by journal 07 Feb, 2025 Editor assigned by journal 07 Feb, 2025 Editor invited by journal 09 Jan, 2025 Submission checks completed at journal 08 Jan, 2025 First submitted to journal 29 Dec, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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