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    <abstract>A century of psychology has found that the trait words people use to describe one another vary, but the relational structure among those traits, which ones go together and which oppose, is strikingly consistent across raters and cultures. We test whether the LLM (Qwen 2.5-7B-Instruct) reproduces this structure in its internal trait representations. From millions of crowd-sourced personality ratings of fictional characters, we build a human implicit-personality matrix over hundreds of traits; from contrastive model activations, we build a matching matrix over the same traits. The two relational structures align strongly (Mantel r = 0.77), and the agreement holds trait by trait as well as in aggregate. Two dominant axes of the model’s trait representations recover the social and intellectual dimensions long known to organize human personality impressions, social warmth and intellectual competence. On held-out dialogue, projecting model activations onto these directions yields personality profiles that agree with human ratings. This work establishes a framework that enables comprehensive, human-grounded comparison between internal model trait geometry and the shared structure of human personality impressions.</abstract>
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%0 Conference Proceedings
%T Implicit Personality Representations in Humans and LLMs
%A Geng, Yilin
%A Abend, Omri
%A Hovy, Eduard
%A Frermann, Lea
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F geng-etal-2026-implicit
%X A century of psychology has found that the trait words people use to describe one another vary, but the relational structure among those traits, which ones go together and which oppose, is strikingly consistent across raters and cultures. We test whether the LLM (Qwen 2.5-7B-Instruct) reproduces this structure in its internal trait representations. From millions of crowd-sourced personality ratings of fictional characters, we build a human implicit-personality matrix over hundreds of traits; from contrastive model activations, we build a matching matrix over the same traits. The two relational structures align strongly (Mantel r = 0.77), and the agreement holds trait by trait as well as in aggregate. Two dominant axes of the model’s trait representations recover the social and intellectual dimensions long known to organize human personality impressions, social warmth and intellectual competence. On held-out dialogue, projecting model activations onto these directions yields personality profiles that agree with human ratings. This work establishes a framework that enables comprehensive, human-grounded comparison between internal model trait geometry and the shared structure of human personality impressions.
%U https://aclanthology.org/2026.findings-emnlp.1508/
%P 32092-32109

Markdown (Informal)

[Implicit Personality Representations in Humans and LLMs](https://aclanthology.org/2026.findings-emnlp.1508/) (Geng et al., Findings 2026)

• Implicit Personality Representations in Humans and LLMs (Geng et al., Findings 2026)

ACL

• Yilin Geng, Omri Abend, Eduard Hovy, and Lea Frermann. 2026. Implicit Personality Representations in Humans and LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 32092–32109, Budapest, Hungary. Association for Computational Linguistics.