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The Better Angels of Machine Personality: How Personality Relates to LLM Safety

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arxiv 2407.12344 v1 pith:JVEXU6D5 submitted 2024-07-17 cs.CL cs.CY

classification cs.CLcs.CY
keywords personalitysafetyllmstraitsabilitiesfairnessperformanceprivacy
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Personality psychologists have analyzed the relationship between personality and safety behaviors in human society. Although Large Language Models (LLMs) demonstrate personality traits, the relationship between personality traits and safety abilities in LLMs still remains a mystery. In this paper, we discover that LLMs' personality traits are closely related to their safety abilities, i.e., toxicity, privacy, and fairness, based on the reliable MBTI-M scale. Meanwhile, the safety alignment generally increases various LLMs' Extraversion, Sensing, and Judging traits. According to such findings, we can edit LLMs' personality traits and improve their safety performance, e.g., inducing personality from ISTJ to ISTP resulted in a relative improvement of approximately 43% and 10% in privacy and fairness performance, respectively. Additionally, we find that LLMs with different personality traits are differentially susceptible to jailbreak. This study pioneers the investigation of LLM safety from a personality perspective, providing new insights into LLM safety enhancement.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Representations to Behaviors: Exploring the Person-Situation-Behavior Triad in LLMs

    cs.CL 2026-07 conditional novelty 6.0 of 10

    SAE features recovered from matched high–low trait behaviors can be steered to bidirectionally shift situational personality expression and produce human-like social benefit–cost patterns in an 8B LLM.

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