titleHuman Psychometric Questionnaires Mischaracterize LLM Behavior
authorsWoojung Song, Dongmin Choi, Yoonah Park, Jongwook Han, Eun-Ju Lee, Yohan Jo
date2026-05-29
venuearXiv (preprint, not peer reviewed)
idarXiv:2509.10078
affiliationSeoul National University
upvotes35 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf
abstractWe examine whether human psychometric questionnaires can serve as reliable tools for characterizing and predicting LLM behavior in everyday user interactions. We analyze eight open-source LLMs by comparing their value and personality profiles derived from two different methods: Likert self-reports on established questionnaires (PVQ-40/21 and BFI-44/10) and generation probabilities over value-laden responses to everyday user queries. The two profiles diverge substantially. Within-construct item consistency, often cited as evidence of stable LLM dispositions, disappears in generation probabilities. We attribute this gap to the fact that explicit lexical cues in established questionnaire items allow models to recognize the target construct and respond in alignment-consistent, socially desirable ways, whereas realistic user queries provide no such cues. In addition, demographic persona prompts shift models' responses to human questionnaires in ways consistent with real human patterns, but no such shifts appear in the generation probabilities of responses to realistic user queries, showing their limited ability to simulate the behaviors of target demographics in real-world user interactions. Overall, our study shows that human psychometric questionnaires are insufficient tools for predicting LLM behavior and suggests generation-based profiling as a more accurate measure.

cite

@misc{song2026human,
  title={Human Psychometric Questionnaires Mischaracterize LLM Behavior},
  author={Woojung Song and Dongmin Choi and Yoonah Park and Jongwook Han and Eun-Ju Lee and Yohan Jo},
  year={2026},
  eprint={2509.10078},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2509.10078}
}