Beyond Fixed Psychological Personas: State Beats Trait, but Language Models are State-Blind

ACL ARR 2026 January Submission7481 Authors

Keywords: psychological personas, state-trait theory, reward models, RLHF, personalization, Big Five personality, contextual variation, fairness, affective computing, dataset

Abstract: User interactions with language models vary due to static properties of the user (trait) and the specific context of the interaction (state). However, existing persona datasets (like PersonaChat, PANDORA etc.) capture only trait, and ignore the impact of state. We introduce Chameleon, a dataset of 5,001 contextual psychological profiles from 1,667 Reddit users, each measured across multiple contexts. Using the Chameleon dataset, we present three key findings. First, inspired by Latent State-Trait theory, we decompose variance and find that 74% is within-person (state) while only 26% is between-person (trait). Second, we find that LLMs are state-blind: they focus on trait only, and produce similar responses regardless of state. Third, we find that reward models react to user state, but inconsistently: different models favor or penalize the same users in opposite directions. We release Chameleon to support research on affective computing, personalized dialogue, and RLHF alignment.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: persona, personalization, user modeling, reward models, RLHF, fairness, psychological text analysis, dataset, evaluation

Contribution Types: NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

Submission Number: 7481