[2511.03235] From Five Dimensions to Many: Large Language Models as Precise and Interpretable Psychological Profilers

Computer Science > Artificial Intelligence arXiv:2511.03235 (cs) [Submitted on 5 Nov 2025 (v1), last revised 23 Mar 2026 (this version, v2)] Title:From Five Dimensions to Many: Large Language Models as Precise and Interpretable Psychological Profilers Authors:Yi-Fei Liu, Yi-Long Lu, Di He, Hang Zhang View a PDF of the paper titled From Five Dimensions to Many: Large Language Models as Precise and Interpretable Psychological Profilers, by Yi-Fei Liu and 3 other authors View PDF HTML (experimental) Abstract:Psychological constructs within individuals are widely believed to be interconnected. We investigated whether and how Large Language Models (LLMs) can model the correlational structure of human psychological traits from minimal quantitative inputs. We prompted various LLMs with Big Five Personality Scale responses from 816 human individuals to role-play their responses on nine other psychological scales. LLMs demonstrated remarkable accuracy in capturing human psychological structure, with the inter-scale correlation patterns from LLM-generated responses strongly aligning with those from human data $(R^2 > 0.89)$. This zero-shot performance substantially exceeded predictions based on semantic similarity and approached the accuracy of machine learning algorithms trained directly on the dataset. Analysis of reasoning traces revealed that LLMs use a systematic two-stage process: First, they transform raw Big Five responses into natural language personality summaries through ...