“镜像”式大语言模型抑郁评估存在标准污染

Computer Science > Computation and Language arXiv:2508.05830 (cs) [Submitted on 7 Aug 2025 (v1), last revised 10 Sep 2026 (this version, v3)] Title:"Mirror" Large Language Model Evaluations of Depression are Criterion Contaminated Authors:Tong Li, Rasiq Hussain, Mehak Gupta, Joshua R. Oltmanns View a PDF of the paper titled "Mirror" Large Language Model Evaluations of Depression are Criterion Contaminated, by Tong Li and 3 other authors View PDF Abstract:Large Language Model (LLM) studies that use language responses elicited from depression assessments to predict scores on those same assessments often report near-perfect prediction of depression. We refer to these as "Mirror" evaluations and demonstrate an applied case of criterion contamination. N = 110 participants completed both structured diagnostic depression interviews (Mirror condition) and life history interviews ("Non-Mirror" condition). LLMs were prompted to predict depression scores in each condition. As expected, Mirror evaluations were near-perfect. However, Non-Mirror evaluations also displayed prediction sizes considered outstanding in psychology. Further, both Mirror and Non-Mirror predictions correlated with Patient Health Questionnaire-9 scores at similar sizes, suggesting the Mirror condition's advantage collapses when predicting an independent depression measurement. Topic modeling revealed differing depression-related themes across interview types. Mirror evaluations are better considered as reliability evaluations than as validity evaluations. Incorporating Non-Mirror approaches in LLM depression assessment may support more valid and clinically-relevant applications. Keywords: large language models, psychological assessment, psychopathology, depression, reliability, validity, criterion contamination Comments: Subjects: Computation and Language (cs.CL); Computers and Society (cs.CY) Cite as: arXiv:2508.05830 [cs.CL] (or arXiv:2508.05830v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2508.05830 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Tong Li [view email] [v1] Thu, 7 Aug 2025 20:13:00 UTC (1,561 KB) [v2] Fri, 17 Oct 2025 20:58:29 UTC (2,379 KB) [v3] Thu, 10 Sep 2026 16:39:44 UTC (2,083 KB) Full-text links: Access Paper: View a PDF of the paper titled "Mirror" Large Language Model Evaluations of Depression are Criterion Contaminated, by Tong Li and 3 other authors View PDF view license Current browse context: cs.CL new | recent | 2025-08 Change to browse by: cs cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)