A Systematic Analysis of the Impact of Persona Steering on LLM Capabilities

• URL: http://arxiv.org/abs/2604.11048v1
• Date: Mon, 13 Apr 2026 06:24:25 GMT
• Title: A Systematic Analysis of the Impact of Persona Steering on LLM Capabilities
• Abstract summary: We employ the Neuron-based Personality Trait Induction (NPTI) framework to induce Big Five personality traits in Large Language Models (LLMs)<n>Our findings reveal that persona induction produces stable, reproducible shifts in cognitive task performance beyond surface-level stylistic changes.<n>We propose Dynamic Persona Routing (DPR), a lightweight query-adaptive strategy that outperforms the best static persona without additional training.
• Score: 21.949978634889302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Imbuing Large Language Models (LLMs) with specific personas is prevalent for tailoring interaction styles, yet the impact on underlying cognitive capabilities remains unexplored. We employ the Neuron-based Personality Trait Induction (NPTI) framework to induce Big Five personality traits in LLMs and evaluate performance across six cognitive benchmarks. Our findings reveal that persona induction produces stable, reproducible shifts in cognitive task performance beyond surface-level stylistic changes. These effects exhibit strong task dependence: certain personalities yield consistent gains on instruction-following, while others impair complex reasoning. Effect magnitude varies systematically by trait dimension, with Openness and Extraversion exerting the most robust influence. Furthermore, LLM effects show 73.68% directional consistency with human personality-cognition relationships. Capitalizing on these regularities, we propose Dynamic Persona Routing (DPR), a lightweight query-adaptive strategy that outperforms the best static persona without additional training.

Related papers

• PERSONA: Dynamic and Compositional Inference-Time Personality Control via Activation Vector Algebra [84.59328460968872]
  Current methods for personality control in Large Language Models rely on static prompting or expensive fine-tuning.<n>We introduce PERSONA, a training-free framework that achieves fine-tuning level performance through direct manipulation of personality vectors.<n>On PersonalityBench, our approach achieves a mean score of 9.60, nearly matching the supervised fine-tuning upper bound of 9.61 without any gradient updates.
  arXiv  Detail & Related papers  (2026-02-17T15:47:58Z)
• Psychological Steering in LLMs: An Evaluation of Effectiveness and Trustworthiness [14.523351279184356]
  Our study spans four models from different LLM families paired with various steering strategies, including prompting, fine-tuning, and representation engineering.<n>Our results indicate that prompting is consistently effective but limited in intensity control, whereas vector injections achieve finer controllability while slightly reducing output quality.<n>Our framework establishes the first holistic evaluation of emotion and personality steering, offering insights into its interpretability and reliability for socially interactive applications.
  arXiv  Detail & Related papers  (2025-10-06T04:49:56Z)
• Vibe Check: Understanding the Effects of LLM-Based Conversational Agents' Personality and Alignment on User Perceptions in Goal-Oriented Tasks [2.1117030125341385]
  Large language models (LLMs) enable conversational agents (CAs) to express distinctive personalities.<n>This study investigates how personality expression levels and user-agent personality alignment influence perceptions in goal-oriented tasks.
  arXiv  Detail & Related papers  (2025-09-11T21:43:49Z)
• The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs [60.15472325639723]
  Personality traits have long been studied as predictors of human behavior.<n>Recent advances in Large Language Models (LLMs) suggest similar patterns may emerge in artificial systems.
  arXiv  Detail & Related papers  (2025-09-03T21:27:10Z)
• A Comparative Study of Large Language Models and Human Personality Traits [6.354326674890978]
  Large Language Models (LLMs) have demonstrated human-like capabilities in language comprehension and generation.<n>This study investigates whether LLMs exhibit personality-like traits and how these traits compare with human personality.
  arXiv  Detail & Related papers  (2025-05-01T15:10:15Z)
• Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models [70.180385882195]
  This paper introduces a personality-aware user simulation for Conversational Recommender Systems (CRSs)<n>The user agent induces customizable personality traits and preferences, while the system agent possesses the persuasion capability to simulate realistic interaction in CRSs.<n> Experimental results demonstrate that state-of-the-art LLMs can effectively generate diverse user responses aligned with specified personality traits.
  arXiv  Detail & Related papers  (2025-04-09T13:21:17Z)
• Investigating the Impact of LLM Personality on Cognitive Bias Manifestation in Automated Decision-Making Tasks [4.65004369765875]
  Personality traits play a crucial role in either amplifying or reducing biases.<n>Conscientiousness and Agreeableness may generally enhance the efficacy of bias mitigation strategies.
  arXiv  Detail & Related papers  (2025-02-20T03:15:54Z)
• Neuron-based Personality Trait Induction in Large Language Models [115.08894603023712]
  Large language models (LLMs) have become increasingly proficient at simulating various personality traits. We present a neuron-based approach for personality trait induction in LLMs.
  arXiv  Detail & Related papers  (2024-10-16T07:47:45Z)
• Evaluating Large Language Models with Psychometrics [59.821829073478376]
  This paper offers a comprehensive benchmark for quantifying psychological constructs of Large Language Models (LLMs)<n>Our work identifies five key psychological constructs -- personality, values, emotional intelligence, theory of mind, and self-efficacy -- assessed through a suite of 13 datasets.<n>We uncover significant discrepancies between LLMs' self-reported traits and their response patterns in real-world scenarios, revealing complexities in their behaviors.
  arXiv  Detail & Related papers  (2024-06-25T16:09:08Z)
• PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection [50.66968526809069]
  We propose a novel personality detection method, called PsyCoT, which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner. Our experiments demonstrate that PsyCoT significantly improves the performance and robustness of GPT-3.5 in personality detection.
  arXiv  Detail & Related papers  (2023-10-31T08:23:33Z)
• Revisiting the Reliability of Psychological Scales on Large Language Models [62.57981196992073]
  This study aims to determine the reliability of applying personality assessments to Large Language Models. Analysis of 2,500 settings per model, including GPT-3.5, GPT-4, Gemini-Pro, and LLaMA-3.1, reveals that various LLMs show consistency in responses to the Big Five Inventory.
  arXiv  Detail & Related papers  (2023-05-31T15:03:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.