Fingerprinting LLMs through Survey Item Factor Correlation: A Case Study on Humor Style Questionnaire
Simon Münker
Abstract
LLMs increasingly engage with psychological instruments, yet how they represent constructs internally remains poorly understood. We introduce a novel approach to "fingerprinting" LLMs through their factor correlation patterns on standardized psychological assessments to deepen the understanding of LLMs constructs representation. Using the Humor Style Questionnaire as a case study, we analyze how six LLMs represent and correlate humor-related constructs to survey participants. Our results show that they exhibit little similarity to human response patterns. In contrast, participants' subsamples demonstrate remarkably high internal consistency. Exploratory graph analysis further confirms that no LLM successfully recovers the four constructs of the Humor Style Questionnaire. These findings suggest that despite advances in natural language capabilities, current LLMs represent psychological constructs in fundamentally different ways than humans, questioning the validity of application as human simulacra.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 5bcd469c-f7bf-4d74-a0e0-c2d8dada34cbBuilds on3
- Flamingo: a Visual Language Model for Few-Shot LearningJean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech et al.NeurIPS 2022 · 6,707 citations
- Moral Foundations of Large Language ModelsMarwa Abdulhai, Gregory Serapio-García, Clément Crepy, Daria Valter et al.EMNLP 2024 · 22 citations
- Unintended Impacts of LLM Alignment on Global RepresentationMichael J. Ryan, William Barr Held, Diyi YangACL 2024
Related papers
- Human Simulacra: Benchmarking the Personification of Large Language ModelsQiujie Xie, Qiming Feng, Tianqi Zhang, Qingqiu Li et al.ICLR 2025
- Assessment and manipulation of latent constructs in pre-trained language models using psychometric scalesMaor Reuben, Ortal Slobodin, Idan-Chaim Cohen, Aviad Elyashar et al.ACL 2025 · 7 citations
- From Five Dimensions to Many: Large Language Models as Precise and Interpretable Psychological ProfilersYi-Fei Liu, Yi-Long Lu, Di He, Hang ZhangICLR 2026 · 6 citations
- HumanLM: Simulating Users with State Alignment Beats Response ImitationShirley Wu, Evelyn Choi, Arpandeep Khatua, Zhanghan Wang et al.ICML 2026
- Assessing the Capabilities of LLMs in Humor: A Multi-dimensional Analysis of Oogiri Generation and EvaluationRitsu Sakabe, Hwichan Kim, Tosho Hirasawa, Mamoru KomachiAAAI 2026
