An LLM-Native Psychometric Instrument Reveals a Self-Report--Behavior Gap Across 25 Models
Abstract
Do large language models' (LLMs') answers to self-report questionnaires predict how they behave? Prior work finds they do not, but it uses human personality inventories, so the gap could reflect borrowed human constructs rather than LLM self-report itself. We test this with a self-report instrument built from LLM-specific behaviors (e.g., over-refusal, unsolicited disclaimers) whose structure is derived bottom-up. Administering 300 items 30 times to 25 LLMs from 17 developers yields five replicable, reliable factors (Tucker $\phi \geq .957$, $\alpha \geq .930$). We compare these self-reports with 2,500 open-ended behavioral samples rated by 151 humans and an LLM-judge ensemble. Humans and judges agree about model behavior ($\bar{r} = .51$), but self-report barely tracks human ratings ($\bar{r} = .09$, 95% CI $[-.07, .18]$) or rater-free text measures, and correcting for criterion unreliability leaves four of five factors near zero. Verbosity is the partial exception ($r = .40$, 71% of its reliability ceiling). On Responsiveness, self-report tracks LLM judges more than humans ($r = .53$ vs. $.18$; Steiger $p = .04$), and controlling for length and formatting does not remove this: agreement between LLM judges and LLM self-report is weak evidence that either tracks human judgment.
