The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responses
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916478029135872 |
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| author | Mieleszczenko-Kowszewicz, Wiktoria Płudowski, Dawid Kołodziejczyk, Filip Świstak, Jakub Sienkiewicz, Julian Biecek, Przemysław |
| author_facet | Mieleszczenko-Kowszewicz, Wiktoria Płudowski, Dawid Kołodziejczyk, Filip Świstak, Jakub Sienkiewicz, Julian Biecek, Przemysław |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06008 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responses Mieleszczenko-Kowszewicz, Wiktoria Płudowski, Dawid Kołodziejczyk, Filip Świstak, Jakub Sienkiewicz, Julian Biecek, Przemysław Computation and Language Artificial Intelligence 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. |
| title | The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responses |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2411.06008 |