arXiv AI By Marcin Moskalewicz, Anna Sterna, Karolina Dro\.zd\.z, Kacper Dudzic, Marek Pokropski, Paula Flores

Computational Phenomenology of Borderline Personality Disorder: A Comparative Evaluation of LLM-Simulated Expert Personas and Human Clinical Experts

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arXiv:2508. 19008v3 Announce Type: replace Abstract: Building on a human-led thematic analysis of clinical life-story interviews (> 150,000 words) with inpatients with Borderline Personality Disorder, this study examines the capacity of large language models (OpenAI's GPT, Google's Gemini, and Anthropic's Claude) to support qualitative clinical analysis.

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arXiv AI
Aug 10

Natural Language Processing Psychometrics

arXiv:2608. 07316v1 Announce Type: cross Abstract: Natural Language Processing (NLP) models predicting mental health outcomes rarely specify what they measure: contextual knowledge, emotional content, or syntactic structure.

By Edoardo Sebastiano De Duro, Emma Franchino, Massimo Stella
arXiv AI
Aug 28

Distinct Profiles of Run-to-Run Score Reliability and Expert-Panel Alignment Across Four LLM Evaluators of Simulated Japanese-Language AI-to-AI Counseling

The study examined how four large language models (GPT‑5.5, Gemini 3.5 Flash, Claude Opus 4.8, and Fable 5) scored 18 simulated Japanese‑language AI‑to‑AI counseling sessions compared to ratings from 15 human counseling experts. Each model evaluated every transcript three times on four motivational‑interviewing‑informed dimensions and overall quality, consistently giving higher scores for softening sustain talk and overall quality than the expert panel, though the magnitude varied by model. Run‑to‑run reliability (intraclass correlation coefficients ranging from .33 to .96) did not predict closer alignment with expert judgments, and the models’ ability to discriminate counselor conditions was distinct from both reliability and alignment.

By Keita Kiuchi, Yoshikazu Fujimoto, Hideyuki Got\=o, Tomonori Hosokawa, Makoto Nishimura, Y\=osuke Sat\=o, Izumi Sezai, Tomohiro Inoue