Psychometric profiling of nine LLMs shows score vectors can identify model identity
anselm · x · 2026-09-25
A new arXiv paper applies a cross-linguistic psychometric framework to nine LLMs—Claude Haiku/Sonnet 4.5, GPT-5.4 (and mini), Gemini 2.5 Flash (and Lite), DeepSeek-V3.2, Doubao-Seed-1.8, and Kimi-K2—administering seven established human instruments (Big Five, moral foundations, Jungian cognitive style, etc.) five times each in English and Chinese.
- Models show structured, model-specific profiles with a shared alignment-shaped pattern: higher prosociality and self-regulation, lower dominance and harmful intent
- Repeated administrations are highly reproducible, and model identity can be recovered from score vectors with high accuracy
- NA responses are structured rather than random, marking where self-report breaks down; language condition and provider origin both affect profile shape
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