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        <title>Exploring the Impact of Personality Traits on LLM Toxicity and Bias</title>
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    <abstract>With the different roles that AI is expected to play in human life, imbuing large language models (LLMs) with different personalities has attracted increasing research interest. While the “personification” enhances human experiences of interactivity and adaptability of LLMs, it gives rise to critical concerns about content safety, particularly regarding bias, sentiment, and toxicity of LLM generation. This study explores how assigning different personality traits to LLMs affects the toxicity and biases of their outputs. Leveraging the widely accepted HEXACO personality framework developed in social psychology, we design experimentally sound prompts to test three LLMs’ performance on three toxic and bias benchmarks. The findings demonstrate the sensitivity of all three models to HEXACO personality traits and, more importantly, a consistent variation in the biases, negative sentiment, and toxicity of their output. In particular, adjusting the levels of several personality traits can effectively reduce bias and toxicity in model performance, similar to humans’ correlations between personality traits and toxic behaviors. The findings highlight the additional need to examine content safety besides the efficiency of training or fine-tuning methods for LLM personification, they also suggest a potential for the adjustment of personalities to be a simple and low-cost method to conduct controlled text generation.</abstract>
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%0 Conference Proceedings
%T Exploring the Impact of Personality Traits on LLM Toxicity and Bias
%A Wang, Shuo
%A Li, Renhao
%A Chen, Xi
%A Yuan, Yulin
%A Yang, Min
%A Wong, Derek F.
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F wang-etal-2025-exploring-impact
%X With the different roles that AI is expected to play in human life, imbuing large language models (LLMs) with different personalities has attracted increasing research interest. While the “personification” enhances human experiences of interactivity and adaptability of LLMs, it gives rise to critical concerns about content safety, particularly regarding bias, sentiment, and toxicity of LLM generation. This study explores how assigning different personality traits to LLMs affects the toxicity and biases of their outputs. Leveraging the widely accepted HEXACO personality framework developed in social psychology, we design experimentally sound prompts to test three LLMs’ performance on three toxic and bias benchmarks. The findings demonstrate the sensitivity of all three models to HEXACO personality traits and, more importantly, a consistent variation in the biases, negative sentiment, and toxicity of their output. In particular, adjusting the levels of several personality traits can effectively reduce bias and toxicity in model performance, similar to humans’ correlations between personality traits and toxic behaviors. The findings highlight the additional need to examine content safety besides the efficiency of training or fine-tuning methods for LLM personification, they also suggest a potential for the adjustment of personalities to be a simple and low-cost method to conduct controlled text generation.
%U https://aclanthology.org/2025.emnlp-main.206/
%P 4125-4143

Markdown (Informal)

[Exploring the Impact of Personality Traits on LLM Toxicity and Bias](https://aclanthology.org/2025.emnlp-main.206/) (Wang et al., EMNLP 2025)

• Exploring the Impact of Personality Traits on LLM Toxicity and Bias (Wang et al., EMNLP 2025)

ACL

• Shuo Wang, Renhao Li, Xi Chen, Yulin Yuan, Min Yang, and Derek F. Wong. 2025. Exploring the Impact of Personality Traits on LLM Toxicity and Bias. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 4125–4143, Suzhou, China. Association for Computational Linguistics.