PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits

• URL: http://arxiv.org/abs/2305.02547v5
• Date: Tue, 2 Apr 2024 06:06:53 GMT
• Title: PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits
• Authors: Hang Jiang, Xiajie Zhang, Xubo Cao, Cynthia Breazeal, Deb Roy, Jad Kabbara,
• Abstract summary: 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%.
• Score: 30.770525830385637
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite the many use cases for large language models (LLMs) in creating personalized chatbots, there has been limited research on evaluating the extent to which the behaviors of personalized LLMs accurately and consistently reflect specific personality traits. We consider studying the behavior of LLM-based agents which we refer to as LLM personas and present a case study with GPT-3.5 and GPT-4 to investigate whether LLMs can generate content that aligns with their assigned personality profiles. To this end, we simulate distinct LLM personas based on the Big Five personality model, have them complete the 44-item Big Five Inventory (BFI) personality test and a story writing task, and then assess their essays with automatic and human evaluations. Results show that LLM personas' self-reported BFI scores are consistent with their designated personality types, with large effect sizes observed across five traits. Additionally, LLM personas' writings have emerging representative linguistic patterns for personality traits when compared with a human writing corpus. Furthermore, human evaluation shows that humans can perceive some personality traits with an accuracy of up to 80%. Interestingly, the accuracy drops significantly when the annotators were informed of AI authorship.

Related papers

• 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)
• 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)
• Driving Generative Agents With Their Personality [0.0]
  This research explores the potential of Large Language Models (LLMs) to utilize psychometric values, specifically personality information, within the context of video game character development. The research shows an LLM can consistently represent a given personality profile, thereby enhancing the human-like characteristics of game characters.
  arXiv  Detail & Related papers  (2024-02-21T21:29:57Z)
• 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)
• Challenging the Validity of Personality Tests for Large Language Models [2.9123921488295768]
  Large language models (LLMs) behave increasingly human-like in text-based interactions. LLMs' responses to personality tests systematically deviate from human responses.
  arXiv  Detail & Related papers  (2023-11-09T11:54:01Z)
• 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)
• 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)
• Can ChatGPT Assess Human Personalities? A General Evaluation Framework [70.90142717649785]
  Large Language Models (LLMs) have produced impressive results in various areas, but their potential human-like psychology is still largely unexplored. This paper presents a generic evaluation framework for LLMs to assess human personalities based on Myers Briggs Type Indicator (MBTI) tests.
  arXiv  Detail & Related papers  (2023-03-01T06:16:14Z)
• Evaluating and Inducing Personality in Pre-trained Language Models [78.19379997967191]
  We draw inspiration from psychometric studies by leveraging human personality theory as a tool for studying machine behaviors. To answer these questions, we introduce the Machine Personality Inventory (MPI) tool for studying machine behaviors. MPI follows standardized personality tests, built upon the Big Five Personality Factors (Big Five) theory and personality assessment inventories. We devise a Personality Prompting (P2) method to induce LLMs with specific personalities in a controllable way.
  arXiv  Detail & Related papers  (2022-05-20T07:32:57Z)

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.