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Generative Personality Simulation via Theory-Informed Structured Interview

Paper · 2026 · Pengda Wang, Huiqi Zou, Han Jiang et al.

Summary

LLMs are increasingly used as stand-ins for human participants, but their simulated answers to personality questionnaires tend to be too uniform. They miss the item-level variation and heterogeneity seen in real people, which limits their usefulness for social science. The authors address this with the Personality Structured Interview (PSI), a set of interview questions built with psychometric scale-development methods and grounded in the Five Factor Model. Instead of turning scale items into yes/no questions, PSI asks behavioral, narrative prompts. The transcripts of human answers are then given to an LLM, which responds to personality inventories (BFI-2) and behavior items (counterproductive work behavior) as that person would.

Evaluation follows measurement theory, checking reliability, structural (factor) validity and external validity at the item, facet and domain levels. Across three experiments, PSI-conditioned simulations correlated moderately and significantly with participants' own self-reports. They also reproduced population-level heterogeneity and factor structure better than competing approaches. Simulated behavior followed expected trends but looked more idealized than real human behavior. The work offers a psychometrically sound route to synthetic data for hard-to-collect populations.

Key points

  • Released resource: a dataset of 357 structured interview transcripts, with code and data at github.com/isle-dev/PSI. Personally identifiable information was removed, and data collection was IRB-approved.
  • Three experiments:
    • reproducing individual-level BFI-2 responses;
    • matching human personality distributions, including descriptive statistics and factor analysis;
    • predicting personality-related behaviors such as counterproductive work behavior.
  • Evaluation framework: a measurement-theory-based procedure (Cronbach; Cronbach & Meehl) that respects the item-to-latent-trait hierarchy and tests reliability, structural validity and external validity.
  • Question ablation: removing single questions lowered R² only slightly, typically within ±0.01. The most important questions differed by target domain, which suggests the questions carry complementary information.
  • Limitations: the tools are human self-report measures, and LLMs do not have a personality themselves. Simulated behaviors remain more idealized than human ones, and generalization to other constructs still needs testing.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

Cite

Cite as Pengda Wang, Huiqi Zou, Han Jiang, Hanjie Chen, Tianjun Sun, Xiaoyuan Yi, Ziang Xiao and Frederick L. Oswald. 2026. Generative Personality Simulation via Theory-Informed Structured Interview. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.eacl-long.82

@inproceedings{wang2026generative,
  title = {{Generative Personality Simulation via Theory-Informed Structured Interview}},
  author = {Pengda Wang, Huiqi Zou, Han Jiang, Hanjie Chen, Tianjun Sun, Xiaoyuan Yi, Ziang Xiao and Frederick L. Oswald},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.82},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.82}
}

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  64. Content of Appendix A Appendix: BFI-2 Scale and Behavioral Measures Scales A.1 BFI-2 Scale . . . . . . . . . . . . A.2 Behavioral Measures Scales . . . I.2 15 15 15 B Appendix: Development and Validation of Personality-Structured Interview Questions 16 B.1 Questions and Development Process 16 B.2 Va… find

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