Apparent Psychological Profiles of Large Language Models are Largely a Measurement Artifact

2026-06-18

AI Summary

Current practices of directly applying human psychometric instruments to large language models (LLMs) to construct “psychological profiles” suffer from fundamental biases that may mislead research on their usability, safety, and agentic behavior. This study employs a psychometric framework to administer multiple personality and risk-preference scales to 56 instruction-tuned LLMs alongside large human samples, integrating self-report questionnaires, behavioral tasks, and variance decomposition into a multimodal assessment system. Findings reveal that 81–90% of inter-model differences stem from directional response biases rather than genuine traits; this bias diminishes with increasing model capability but persists nonetheless. Scale reliability is almost entirely predicted by a newly proposed metric—“response orthogonality.” These results demonstrate that LLM “psychological profiles” can be artificially manipulated through item selection, exposing critical limitations in prevailing evaluation paradigms.

Abstract

Psychological instruments designed for humans are increasingly used to assign large language models (LLMs) stable psychological profiles that affect their usability, safety assessment, and use as proxies for human participants in research. Using a formal psychometric framework, we show that these profiles are largely a measurement artifact. Administering a battery of personality and risk-preference instruments spanning self-reports and behavioral tasks to 56 instruction-tuned LLMs alongside large human reference samples, we report four findings. First, differences between models are driven not by the traits an instrument targets but by a directional response bias, a tendency to respond toward one end of the scale, or one labeled option, regardless of item content; a variance decomposition attributes 81-90% of between-model variation to this bias, against 9-16% in humans. Second, the bias declines with model capability but is not eliminated by it. Third, because bias rather than trait drives responding, an instrument's apparent reliability is almost entirely predicted by its response orthogonality, a term we coin for the proportion of items for which trait and bias point in opposite directions. Fourth, the profile a model appears to have shifts with the items used and can be manufactured through item selection. These results demonstrate that the apparent psychological profiles of LLMs are artifacts of the instrument used to measure them, not properties of the models themselves. As instruments borrowed from human psychology are rarely fully orthogonal and may inherently lack validity for LLMs, we call for dedicated assessments centered on response orthogonality.

Problem

• large language models
• psychological profiles
• measurement artifact
• response bias
• psychometric instruments

Innovation

• measurement artifact
• response bias
• response orthogonality
• large language models
• psychometric validity