A Large Foundation Model for Assessing Spatially Distributed Personality Traits

Avi Bleiweiss
Proceedings of Large Foundation Models for Educational Assessment, PMLR 264:173-185, 2025.

Abstract

We explored emulating textually encoded personality information in a large language model. Given its predominant empirical validation, we chose the five-factor model of personality compiled for a broad range of natural languages. Our study assessed personality traits from a multicultural viewpoint over a diverse set of thirty universal contexts. Thus, contributing to the wider comprehension of generalizing relationships among personality traits across cultures. We administered psychometric tests to the language model, examined links between location and personality, and cross validated measures at various levels of trait hierarchy.

Cite this Paper

BibTeX
@InProceedings{pmlr-v264-bleiweiss25a, title = {A Large Foundation Model for Assessing Spatially Distributed Personality Traits}, author = {Bleiweiss, Avi}, booktitle = {Proceedings of Large Foundation Models for Educational Assessment}, pages = {173--185}, year = {2025}, editor = {Li, Sheng and Cui, Zhongmin and Lu, Jiasen and Harris, Deborah and Jing, Shumin}, volume = {264}, series = {Proceedings of Machine Learning Research}, month = {15--16 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v264/main/assets/bleiweiss25a/bleiweiss25a.pdf}, url = {https://proceedings.mlr.press/v264/bleiweiss25a.html}, abstract = {We explored emulating textually encoded personality information in a large language model. Given its predominant empirical validation, we chose the five-factor model of personality compiled for a broad range of natural languages. Our study assessed personality traits from a multicultural viewpoint over a diverse set of thirty universal contexts. Thus, contributing to the wider comprehension of generalizing relationships among personality traits across cultures. We administered psychometric tests to the language model, examined links between location and personality, and cross validated measures at various levels of trait hierarchy.} }
Endnote
%0 Conference Paper %T A Large Foundation Model for Assessing Spatially Distributed Personality Traits %A Avi Bleiweiss %B Proceedings of Large Foundation Models for Educational Assessment %C Proceedings of Machine Learning Research %D 2025 %E Sheng Li %E Zhongmin Cui %E Jiasen Lu %E Deborah Harris %E Shumin Jing %F pmlr-v264-bleiweiss25a %I PMLR %P 173--185 %U https://proceedings.mlr.press/v264/bleiweiss25a.html %V 264 %X We explored emulating textually encoded personality information in a large language model. Given its predominant empirical validation, we chose the five-factor model of personality compiled for a broad range of natural languages. Our study assessed personality traits from a multicultural viewpoint over a diverse set of thirty universal contexts. Thus, contributing to the wider comprehension of generalizing relationships among personality traits across cultures. We administered psychometric tests to the language model, examined links between location and personality, and cross validated measures at various levels of trait hierarchy.
APA
Bleiweiss, A.. (2025). A Large Foundation Model for Assessing Spatially Distributed Personality Traits. Proceedings of Large Foundation Models for Educational Assessment, in Proceedings of Machine Learning Research 264:173-185 Available from https://proceedings.mlr.press/v264/bleiweiss25a.html.

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