Persona Cartography: Charting Language Model Personality Traits in Weight Space

2026-07-08

AI Summary

Large language models exhibit stable, personality-like behavioral patterns that influence their generalization and safety, yet effective methods for disentangling, measuring, and controlling these traits remain lacking. This work models model personality as positions in the OCEAN (Big Five) trait space and constructs, for the first time, an editable and composable personality map in weight space. By leveraging low-rank adapters (LoRA), the approach enables monotonic adjustment of specific personality dimensions and supports linear composition to form hybrid personalities. Validated through unsupervised psychometrics, LLM-based evaluators, and human judgment benchmarks across models ranging from 4B to 32B parameters, the method significantly modulates downstream safety-related behaviors—such as neuroticism affecting frustration tolerance and agreeableness influencing flattery tendencies—while preserving general capabilities, thereby establishing a principled bridge between personality measurement, model editing, and safety alignment.

Abstract

Large language models exhibit recurring behavioural patterns -- personas -- that shape generalisation and safety, but we lack reliable tools for decomposing, measuring, and controlling them. Our central insight is to treat personas as positions in a space of behavioural traits, using the OCEAN framework to describe model personas in terms of Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. We train low-rank adapters to amplify or suppress individual traits, and evaluate their effects using an LLM-judge calibrated against a human-validated panel, trait-specific multiple-choice benchmarks, and standard capability evaluations. Across six models from three families (4B-32B), we find that each adapter moves its target trait largely monotonically with scale, combines approximately additively with other adapters to construct mixed personas, and preserves performance on capability benchmarks at moderate scales. We further show that the induced trait axes affect safety-relevant behaviour in downstream evaluations: for example, moving along neuroticism and agreeableness axes affects frustration and sycophancy respectively. We also introduce an unsupervised psychometric pipeline that recovers four interpretable behavioural factors (tone, initiative, didacticism, epistemic caution) from model rollouts. Persona control can then be considered in terms of learning, scaling, and composing traits in weight space, providing a bridge between personality measurement, model editing, and safety.

Problem

• persona
• personality traits
• language models
• behavioral patterns
• model safety

Innovation

• persona control
• OCEAN personality
• low-rank adapters
• weight space editing
• unsupervised psychometrics