The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responses
- URL: http://arxiv.org/abs/2411.06008v2
- Date: Tue, 12 Nov 2024 14:30:28 GMT
- Title: The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responses
- Authors: Wiktoria Mieleszczenko-Kowszewicz, Dawid Płudowski, Filip Kołodziejczyk, Jakub Świstak, Julian Sienkiewicz, Przemysław Biecek,
- Abstract summary: We identify 13 linguistic features crucial for influencing personalities across different levels of the Big Five model of personality.
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.
- Score: 0.0
- License:
- Abstract: 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.
Related papers
- Evaluating Personality Traits in Large Language Models: Insights from Psychological Questionnaires [3.6001840369062386]
This work applies psychological tools to Large Language Models in diverse scenarios to generate personality profiles.
Our findings reveal that LLMs exhibit unique traits, varying characteristics, and distinct personality profiles even within the same family of models.
arXiv Detail & Related papers (2025-02-07T16:12:52Z) - LMLPA: Language Model Linguistic Personality Assessment [11.599282127259736]
Large Language Models (LLMs) are increasingly used in everyday life and research.
measuring the personality of a given LLM is currently a challenge.
This paper introduces the Language Model Linguistic Personality Assessment (LMLPA), a system designed to evaluate the linguistic personalities of LLMs.
arXiv Detail & Related papers (2024-10-23T07:48:51Z) - Revealing Personality Traits: A New Benchmark Dataset for Explainable Personality Recognition on Dialogues [63.936654900356004]
Personality recognition aims to identify the personality traits implied in user data such as dialogues and social media posts.
We propose a novel task named Explainable Personality Recognition, aiming to reveal the reasoning process as supporting evidence of the personality trait.
arXiv Detail & Related papers (2024-09-29T14:41:43Z) - Rediscovering the Latent Dimensions of Personality with Large Language Models as Trait Descriptors [4.814107439144414]
We introduce a novel approach that uncovers latent personality dimensions in large language models (LLMs)
Our experiments show that LLMs "rediscover" core personality traits such as extraversion, agreeableness, conscientiousness, neuroticism, and openness without relying on direct questionnaire inputs.
We can use the derived principal components to assess personality along the Big Five dimensions, and achieve improvements in average personality prediction accuracy of up to 5% over fine-tuned models.
arXiv Detail & Related papers (2024-09-16T00:24:40Z) - Secret Keepers: The Impact of LLMs on Linguistic Markers of Personal Traits [6.886654996060662]
We investigate the impact of Large Language Models (LLMs) on the linguistic markers of demographic and psychological traits.
Our findings indicate that although the use of LLMs slightly reduces the predictive power of linguistic patterns over authors' personal traits, the significant changes are infrequent.
arXiv Detail & Related papers (2024-03-30T06:49:17Z) - LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced
Personality Detection Model [58.887561071010985]
Personality detection aims to detect one's personality traits underlying in social media posts.
Most existing methods learn post features directly by fine-tuning the pre-trained language models.
We propose a large language model (LLM) based text augmentation enhanced personality detection model.
arXiv Detail & Related papers (2024-03-12T12:10:18Z) - LLMs Simulate Big Five Personality Traits: Further Evidence [51.13560635563004]
We analyze the personality traits simulated by Llama2, GPT4, and Mixtral.
This contributes to the broader understanding of the capabilities of LLMs to simulate personality traits.
arXiv Detail & Related papers (2024-01-31T13:45:25Z) - PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for
Personality Detection [50.66968526809069]
We propose a novel personality detection method, called PsyCoT, which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner.
Our experiments demonstrate that PsyCoT significantly improves the performance and robustness of GPT-3.5 in personality detection.
arXiv Detail & Related papers (2023-10-31T08:23:33Z) - Editing Personality for Large Language Models [73.59001811199823]
This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs)
We construct PersonalityEdit, a new benchmark dataset to address this task.
arXiv Detail & Related papers (2023-10-03T16:02:36Z) - Revisiting the Reliability of Psychological Scales on Large Language Models [62.57981196992073]
This study aims to determine the reliability of applying personality assessments to Large Language Models.
Analysis of 2,500 settings per model, including GPT-3.5, GPT-4, Gemini-Pro, and LLaMA-3.1, reveals that various LLMs show consistency in responses to the Big Five Inventory.
arXiv Detail & Related papers (2023-05-31T15:03:28Z) - PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits [30.770525830385637]
We study the behavior of large language models (LLMs) based on the Big Five personality model.
Results show that LLM personas' self-reported BFI scores are consistent with their designated personality types.
Human evaluation shows that humans can perceive some personality traits with an accuracy of up to 80%.
arXiv Detail & Related papers (2023-05-04T04:58:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.