LLM会根据人格特质调整语言风格，实现精准说服。
The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responses
- 分析19个模型在五大家族中的语言特征变化
- 发现神经质用焦虑词，尽责性用成就词，开放性减少思辨词
- 部分模型擅长特定人格适配，仅一个模型能有效回应神经质
本研究探讨大型语言模型（LLMs）如何调整语言特征以生成个性化说服性输出。尽管已有研究证明LLMs可个性化输出，但其说服能力的语言特征仍不明确。我们识别出13种关键语言特征，这些特征在大五人格模型的各个维度上影响个性表现。通过分析包含人格特征信息的提示对19个不同模型家族的输出影响，发现模型在应对神经质时使用更多焦虑相关词汇，对尽责性则增加成就相关词汇，而对开放性则减少认知过程词汇。某些模型家族在适应开放性方面表现突出，另一些则在尽责性上更优，但仅有一个模型能有效适应神经质。研究揭示了LLMs如何基于提示中的人格线索调整回应，表明其在影响接收者心智与福祉方面具有潜在说服力。
原文摘要 · Abstract (English)
This study explores how the Large Language Models (LLMs) adjust linguistic features to create personalized persuasive outputs. While research showed that LLMs personalize outputs, a gap remains in understanding the linguistic features of their persuasive capabilities. We identified 13 linguistic features crucial for influencing personalities across different levels of the Big Five model of personality. We analyzed how prompts with personality trait information influenced the output of 19 LLMs across five model families. The findings show that models use more anxiety-related words for neuroticism, increase achievement-related words for conscientiousness, and employ fewer cognitive processes words for openness to experience. Some model families excel at adapting language for openness to experience, others for conscientiousness, while only one model adapts language for neuroticism. Our findings show how LLMs tailor responses based on personality cues in prompts, indicating their potential to create persuasive content affecting the mind and well-being of the recipients.
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