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LLMs Simulate Big Five Personality Traits: Further Evidence
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An empirical investigation into the simulation of the Big Five personality traits by large language models (LLMs), namely Llama2, GPT4, and Mixtral, is presented. We analyze the personality traits simulated by these models and their stability. This contributes to the broader understanding of the capabilities of LLMs to simulate personality traits and the respective implications for personalized human-computer interaction.
Forward citations
Cited by 4 Pith papers
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PTEI: Integrating Personality Traits to Enhance Emotional Intelligence in Large Language Models
Personality-aware prompting plus contrastive retrieval of aligned scenarios measurably lifts LLM accuracy on EmoBench emotional-understanding tasks, especially for GPT models with CoT.
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On the Adaptive Psychological Persuasion of Large Language Models
An adaptive preference-optimization method helps LLM persuaders choose among 11 psychological strategies, improving persuasion success on counterfactual facts while preserving general capability.
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Can LLMs Generate Behaviors for Embodied Virtual Agents Based on Personality Traits?
LLM prompting can steer both speech and nonverbal cues of virtual agents toward intended extraversion levels, with human observers detecting the difference.
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Exploring the Potential of Large Language Models to Simulate Personality
LLMs prompted with Big Five trait scores can respond consistently to personality questionnaires but generate free text that often fails to express the prompted trait, especially Neuroticism.
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