Rediscovering the Latent Dimensions of Personality with Large Language Models as Trait Descriptors

• URL: http://arxiv.org/abs/2409.09905v1
• Date: Mon, 16 Sep 2024 00:24:40 GMT
• Title: Rediscovering the Latent Dimensions of Personality with Large Language Models as Trait Descriptors
• Authors: Joseph Suh, Suhong Moon, Minwoo Kang, David M. Chan,
• Abstract summary: 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.
• Score: 4.814107439144414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Assessing personality traits using large language models (LLMs) has emerged as an interesting and challenging area of research. While previous methods employ explicit questionnaires, often derived from the Big Five model of personality, we hypothesize that LLMs implicitly encode notions of personality when modeling next-token responses. To demonstrate this, we introduce a novel approach that uncovers latent personality dimensions in LLMs by applying singular value de-composition (SVD) to the log-probabilities of trait-descriptive adjectives. Our experiments show that LLMs "rediscover" core personality traits such as extraversion, agreeableness, conscientiousness, neuroticism, and openness without relying on direct questionnaire inputs, with the top-5 factors corresponding to Big Five traits explaining 74.3% of the variance in the latent space. Moreover, 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, and up to 21% over direct LLM-based scoring techniques.

Related papers

• The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responses [0.0]
  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.
  arXiv  Detail & Related papers  (2024-11-08T23:02:59Z)
• Neuron-based Personality Trait Induction in Large Language Models [115.08894603023712]
  Large language models (LLMs) have become increasingly proficient at simulating various personality traits. We present a neuron-based approach for personality trait induction in LLMs.
  arXiv  Detail & Related papers  (2024-10-16T07:47:45Z)
• Predicting the Big Five Personality Traits in Chinese Counselling Dialogues Using Large Language Models [14.04596228819108]
  This study exams whether Large Language Models (LLMs) can predict the Big Five personality traits directly from counseling dialogues. Our framework applies role-play and questionnaire-based prompting to condition LLMs on counseling sessions. Our model achieves a 130.95% improvement, surpassing the state-of-the-art Qwen1.5-110B by 36.94% in personality prediction validity.
  arXiv  Detail & Related papers  (2024-06-25T05:30:55Z)
• 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)
• Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension [63.330262740414646]
  We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs) We suggest investigating internal activations and quantifying LLM's truthfulness using the local intrinsic dimension (LID) of model activations.
  arXiv  Detail & Related papers  (2024-02-28T04:56:21Z)
• Eliciting Personality Traits in Large Language Models [0.0]
  Large Language Models (LLMs) are increasingly being utilized by both candidates and employers in the recruitment context. This study seeks to obtain a better understanding of such models by examining their output variations based on different input prompts.
  arXiv  Detail & Related papers  (2024-02-13T10:09:00Z)
• 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)
• 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.