How Do Personality Traits Affect LLM Performance on a Variety of Tasks?

Abstract

Large Language Models (LLMs) have demonstrated impressive performance across diverse natural language processing (NLP) and reasoning tasks, yet the influence of psychological factors, such as personality traits, on their capabilities remains underexplored. Drawing on the Big Five personality framework, we investigate how personality configurations affect LLM performance across five task categories: interdisciplinary expert knowledge, safety and harmfulness detection, code generation, mathematical reasoning, and scientific problem solving. We control personality traits using two approaches: prompt-based induction with expert-crafted prompts and low-rank adaptation (LoRA) fine-tuning on a newly constructed dataset of 20,000 personality-conditioned instructions. Experiments on seven LLMs reveal systematic, task-dependent effects of personality. High Neuroticism consistently degrades robustness and generalization, while high Conscientiousness improves stability, particularly in safety-critical contexts. Larger models show stronger resilience under personality perturbations, and prompt-based control achieves a better balance between trait alignment and task performance than LoRA fine-tuning. These findings highlight the trade-offs between controllability, stability, and generalization in personality-aware LLMs.

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