"Mirror" Large Language Model Evaluations of Depression are Criterion Contaminated — Paper

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

Authors 4

Tong Li, Rasiq Hussain, Mehak Gupta, Joshua R. Oltmanns

Specification

Official page: arxiv.org/abs/2508.05830
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
arXiv id: 2508.05830
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Categories: cs.CL, cs.CY
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
PDF: arxiv.org/pdf/2508.05830
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Primary category: cs.CL
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh

Each value shows its source, tier and observation time. Conflicting claims are kept side by side and flagged — never averaged. How AI Atlas records facts →

Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

1 h ago

Conflicts

None

No models linked to this paper yet.

Authors: Tong Li, Rasiq Hussain, Mehak Gupta

As of

Rewind the record: see this entity's attributes exactly as AI Atlas knew them on a given day.

Claim history · PDF

1 claims · 1 propertiesShow all properties

PDFpdf_url1

Claim history for PDF
Value	Valid from → to	Status	Source	Confidence	Extractor
https://arxiv.org/pdf/2508.05830	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaper"Mirror" Large Language Model Evaluations of Depression are Criterion Contaminated
New paper: "Mirror" Large Language Model Evaluations of Depression are Criterion Contaminated
arxiv
rss.arxiv.org

Full timeline →

Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CL	feed	T1· Official	1 h ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.