# Personality Expression Across Contexts: Linguistic and Behavioral Variation in LLM Agents

arXiv: https://arxiv.org/abs/2602.01063 · HTML: https://mlsanity.com/paper/2602.01063/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 1 Feb 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 16 of 16)

- [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023)
- [Personality Traits in Large Language Models](https://mlsanity.com/paper/2307.00184/) (2023)
- [Evaluating and Inducing Personality in Pre-trained Language Models](https://mlsanity.com/paper/2206.07550/) (2022)
- [Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues](https://mlsanity.com/paper/2402.13550/) (2024)
- [LLMs with Personalities in Multi-issue Negotiation Games](https://mlsanity.com/paper/2405.05248/) (2024)
- [Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles](https://mlsanity.com/paper/2010.01309/) (2020)
- [GPT-4 Emulates Average-Human Emotional Cognition from a Third-Person Perspective](https://mlsanity.com/paper/2408.13718/) (2024)
- [How Personality Traits Influence Negotiation Outcomes? A Simulation based on Large Language Models](https://mlsanity.com/paper/2407.11549/) (2024)
- [KODIS: A Multicultural Dispute Resolution Dialogue Corpus](https://mlsanity.com/paper/2504.12723/) (2025)
- [Psychological Steering in LLMs: An Evaluation of Effectiveness and Trustworthiness](https://mlsanity.com/paper/2510.04484/) (2025)
- [Evaluating Behavioral Alignment in Conflict Dialogue: A Multi-Dimensional Comparison of LLM Agents and Humans](https://mlsanity.com/paper/2509.16394/) (2025)
- [Can LLMs Generate Behaviors for Embodied Virtual Agents Based on Personality Traits?](https://mlsanity.com/paper/2508.21087/) (2025)
- [Knowledge-based Emotion Recognition using Large Language Models](https://mlsanity.com/paper/2408.04123/) (2024)
- [Beyond Self-Reports: Multi-Observer Agents for Personality Assessment in Large Language Models](https://mlsanity.com/paper/2504.08399/) (2025)
- [Are Economists Always More Introverted? Analyzing Consistency in Persona-Assigned LLMs](https://mlsanity.com/paper/2506.02659/) (2025)
- [Since U Been Gone: Augmenting Context-Aware Transcriptions for Re-engaging in Immersive VR Meetings](https://mlsanity.com/paper/2503.16739/) (2025)

## Built on by (most cited first, 4 of 4)

- [Stories of Your Life as Others: A Round-Trip Evaluation of LLM-Generated Life Stories Conditioned on Rich Psychometric Profiles](https://mlsanity.com/paper/2604.06071/) (2026)
- [Emergi-PersonaOS: A Persona Agent Operating System for Situational Adaptation and Controllable Evolution](https://mlsanity.com/paper/2609.27417/) (2026)
- [Reach Into The CHOIR: Free-List Elicitation Uncovers Distinct Model Voices in LLM Ensembles](https://mlsanity.com/paper/2609.38448/) (2026)
- [Back in Style: A Sociolinguistic Approach to Authoring and Measuring Persona Fidelity in User Simulation](https://mlsanity.com/paper/2610.10988/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) can be conditioned with explicit personality prompts, yet their behavioral realization often varies depending on context. This study examines how identical personality prompts lead to distinct linguistic, behavioral, and emotional outcomes across four conversational settings: ice-breaking, negotiation, group decision, and empathy tasks. Results show that contextual cues systematically influence both personality expression and emotional tone, suggesting that the same traits are expressed differently depending on social and affective demands. This raises an important question for LLM-based dialogue agents: whether such variations reflect inconsistency or context-sensitive adaptation akin to human behavior. Viewed through the lens of Whole Trait Theory, these findings highlight that LLMs exhibit context-sensitive rather than fixed personality expression, adapting flexibly to social interaction goals and affective conditions.