@inproceedings{song-etal-2026-human,
title = "Human Psychometric Questionnaires Mischaracterize {LLM} Behavior",
author = "Song, Woojung and
Choi, Dongmin and
Park, Yoonah and
Han, Jongwook and
Lee, Eun-Ju and
Jo, Yohan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1993/",
pages = "45356--45401",
ISBN = "979-8-89176-459-0",
abstract = "We 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 find that established questionnaire items contain explicit lexical cues that allow models to recognize the target construct and respond in alignment-consistent, socially desirable ways, whereas realistic user queries contain far less recognizable 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 setting, highlighting that human questionnaires overestimate LLMs' ability to faithfully reproduce expected psychological traits when role-playing demographic personas. Overall, our study indicates that questionnaire scores alone should not be treated as evidence of LLMs' response tendencies in realistic user interactions, and supports generation-probability profiling with ecologically valid items as a complementary behavioral measure."
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%0 Conference Proceedings
%T Human Psychometric Questionnaires Mischaracterize LLM Behavior
%A Song, Woojung
%A Choi, Dongmin
%A Park, Yoonah
%A Han, Jongwook
%A Lee, Eun-Ju
%A Jo, Yohan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F song-etal-2026-human
%X We 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 find that established questionnaire items contain explicit lexical cues that allow models to recognize the target construct and respond in alignment-consistent, socially desirable ways, whereas realistic user queries contain far less recognizable 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 setting, highlighting that human questionnaires overestimate LLMs’ ability to faithfully reproduce expected psychological traits when role-playing demographic personas. Overall, our study indicates that questionnaire scores alone should not be treated as evidence of LLMs’ response tendencies in realistic user interactions, and supports generation-probability profiling with ecologically valid items as a complementary behavioral measure.
%U https://aclanthology.org/2026.emnlp-main.1993/
%P 45356-45401
Markdown (Informal)
[Human Psychometric Questionnaires Mischaracterize LLM Behavior](https://aclanthology.org/2026.emnlp-main.1993/) (Song et al., EMNLP 2026)
ACL
- Woojung Song, Dongmin Choi, Yoonah Park, Jongwook Han, Eun-Ju Lee, and Yohan Jo. 2026. Human Psychometric Questionnaires Mischaracterize LLM Behavior. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 45356–45401, Budapest, Hungary. Association for Computational Linguistics.