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

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Large Language ModelPsychological ProfilingHuman SimulationZero-Shot PredictionReasoning Trace Analysis
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 (R² > 0.88). 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 information selection and compression, analogous to generating sufficient statistics. Second, they generate target scale responses based on reasoning from these summaries. For information selection, LLMs identify the same key personality factors as trained algorithms, though they fail to differentiate item importance within factors. The resulting compressed summaries are not merely redundant representations but capture synergistic information—adding them to original scores enhances prediction alignment, suggesting they encode emergent, second-order patterns of trait interplay. Our findings demonstrate that LLMs can precisely predict individual participants' psychological traits from minimal data through a process of abstraction and reasoning, offering both a powerful tool for psychological simulation and valuable insights into their emergent reasoning capabilities.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper investigates whether large language models can predict correlational structures among psychological traits when prompted with minimal quantitative inputs (Big Five responses). It resides in the 'Large Language Model-Based Personality Inference' leaf, which contains only three papers total, indicating a sparse and emerging research direction. This leaf sits within the broader 'Computational and Machine Learning Approaches to Personality Prediction' branch, distinguishing itself from classical machine learning methods and traditional NLP approaches by focusing specifically on generative AI capabilities for personality assessment.

The taxonomy reveals neighboring leaves dedicated to 'Classical Machine Learning for Personality Prediction' (four papers) and 'Natural Language Processing for Trait Assessment' (five papers), both employing non-LLM computational methods. The paper's approach diverges from these by leveraging zero-shot reasoning in large language models rather than supervised learning or feature extraction from unstructured text. The broader field also includes extensive psychometric validation work and trait-outcome prediction studies, but the paper's computational focus and minimal-input paradigm position it distinctly within the emerging LLM-based inference cluster.

Among 23 candidates examined across three contributions, none were found to clearly refute the paper's claims. The 'second-order structural alignment evaluation method' examined 10 candidates with zero refutations, suggesting limited prior work on this specific evaluation approach. The 'structural amplification phenomenon' contribution also examined 10 candidates without refutation, indicating potential novelty in characterizing how LLMs amplify correlational patterns. The 'two-stage reasoning process decomposition' examined three candidates, again with no clear prior overlap. These statistics reflect a focused search scope rather than exhaustive coverage.

Given the limited search scope of 23 candidates and the sparse three-paper leaf, the work appears to occupy relatively unexplored territory within LLM-based personality inference. The absence of refuting prior work across all contributions suggests either genuine novelty or gaps in the candidate pool. The taxonomy context confirms this is an emerging subfield, though the small search scale means substantial related work may exist beyond the examined candidates.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
23
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Predicting psychological trait correlations from personality scale responses. The field encompasses diverse approaches to understanding and forecasting personality traits, organized into four main branches. Computational and Machine Learning Approaches to Personality Prediction leverage modern algorithms—including deep learning and large language models—to infer traits from digital footprints, text, or structured responses. Psychometric Development and Validation of Personality Instruments focuses on constructing and refining measurement tools, ensuring reliability and validity across populations and languages, as seen in works like Personality Inventory Construction[3] and various cultural adaptations. Personality Trait Prediction of Life Outcomes and Well-Being examines how traits forecast real-world behaviors, health, and satisfaction, with studies such as Facets Predict Wellbeing[6] and Personality Predicts Wellbeing[28] demonstrating predictive power. Methodological and Theoretical Advances in Personality Assessment addresses foundational issues in measurement theory, response validity, and the evolution of assessment frameworks. Recent computational work has intensified around large language model-based inference, exploring whether LLMs can simulate or predict personality profiles from minimal input. LLMs Psychological Profilers[0] sits squarely within this emerging cluster, investigating how modern language models function as psychological profilers by predicting trait correlations. This approach contrasts with earlier NLP methods like NLP Personality Prediction[11] and shares methodological kinship with LLM Embeddings Personality[1] and LLM HEXACO Simulation[43], which similarly probe LLM capabilities for personality assessment. A central question across these studies is whether model-generated predictions capture genuine psychometric structure or reflect superficial pattern matching. Meanwhile, traditional psychometric branches continue refining instruments and exploring trait-outcome links, highlighting ongoing tensions between data-driven prediction and theory-grounded measurement.

Claimed Contributions

Second-order structural alignment evaluation method for psychological trait prediction

The authors introduce a novel evaluation methodology that moves beyond first-order prediction accuracy to assess how well LLMs reconstruct the entire correlational structure (nomothetic network) of psychological traits. This second-order analysis compares inter-scale correlation patterns rather than individual trait predictions.

10 retrieved papers
Discovery and characterization of structural amplification phenomenon in LLM psychological reasoning

The authors identify and characterize a systematic phenomenon where LLMs reconstruct an idealized, linearly amplified version of human psychological trait correlations when predicting from sparse Big Five personality inputs. This structural amplification (regression slope greater than 1.0) represents a form of noise filtering that produces theory-consistent representations.

10 retrieved papers
Two-stage reasoning process decomposition through meta-prompt analysis

The authors develop a meta-prompt methodology to parse LLM reasoning traces, revealing that models employ a concept-driven information selection strategy (prioritizing high-level personality factors) followed by information compression into predictively potent natural language summaries that contain emergent, synergistic information beyond the original numerical inputs.

3 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Second-order structural alignment evaluation method for psychological trait prediction

The authors introduce a novel evaluation methodology that moves beyond first-order prediction accuracy to assess how well LLMs reconstruct the entire correlational structure (nomothetic network) of psychological traits. This second-order analysis compares inter-scale correlation patterns rather than individual trait predictions.

Contribution

Discovery and characterization of structural amplification phenomenon in LLM psychological reasoning

The authors identify and characterize a systematic phenomenon where LLMs reconstruct an idealized, linearly amplified version of human psychological trait correlations when predicting from sparse Big Five personality inputs. This structural amplification (regression slope greater than 1.0) represents a form of noise filtering that produces theory-consistent representations.

Contribution

Two-stage reasoning process decomposition through meta-prompt analysis

The authors develop a meta-prompt methodology to parse LLM reasoning traces, revealing that models employ a concept-driven information selection strategy (prioritizing high-level personality factors) followed by information compression into predictively potent natural language summaries that contain emergent, synergistic information beyond the original numerical inputs.

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