Open Models, Closed Minds? On Agents Capabilities in Mimicking Human Personalities through Open Large Language Models

• URL: http://arxiv.org/abs/2401.07115v2
• Date: Sun, 23 Jun 2024 19:53:33 GMT
• Title: Open Models, Closed Minds? On Agents Capabilities in Mimicking Human Personalities through Open Large Language Models
• Authors: Lucio La Cava, Andrea Tagarelli,
• Abstract summary: The work represents a step up in understanding the dense relationship between NLP and human psychology through the lens of Open LLMs. Our approach involves evaluating the intrinsic personality traits of Open LLM agents and determining the extent to which these agents can mimic human personalities.
• Score: 4.742123770879715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of unveiling human-like behaviors in Large Language Models (LLMs) has led to a closer connection between NLP and human psychology. Scholars have been studying the inherent personalities exhibited by LLMs and attempting to incorporate human traits and behaviors into them. However, these efforts have primarily focused on commercially-licensed LLMs, neglecting the widespread use and notable advancements seen in Open LLMs. This work aims to address this gap by employing a set of 12 LLM Agents based on the most representative Open models and subject them to a series of assessments concerning the Myers-Briggs Type Indicator (MBTI) test and the Big Five Inventory (BFI) test. Our approach involves evaluating the intrinsic personality traits of Open LLM agents and determining the extent to which these agents can mimic human personalities when conditioned by specific personalities and roles. Our findings unveil that $(i)$ each Open LLM agent showcases distinct human personalities; $(ii)$ personality-conditioned prompting produces varying effects on the agents, with only few successfully mirroring the imposed personality, while most of them being ``closed-minded'' (i.e., they retain their intrinsic traits); and $(iii)$ combining role and personality conditioning can enhance the agents' ability to mimic human personalities. Our work represents a step up in understanding the dense relationship between NLP and human psychology through the lens of Open LLMs.

Related papers

• The Better Angels of Machine Personality: How Personality Relates to LLM Safety [46.30207266304056]
  The relationship between personality traits and safety abilities in Large Language Models (LLMs) remains a mystery. We discover that LLMs' personality traits are closely related to their safety abilities. inducing personality from ISTJ to ISTP resulted in a relative improvement of approximately 43% and 10% in privacy and fairness performance.
  arXiv  Detail & Related papers  (2024-07-17T06:36:29Z)
• Quantifying AI Psychology: A Psychometrics Benchmark for Large Language Models [57.518784855080334]
  Large Language Models (LLMs) have demonstrated exceptional task-solving capabilities, increasingly adopting roles akin to human-like assistants. This paper presents a framework for investigating psychology dimension in LLMs, including psychological identification, assessment dataset curation, and assessment with results validation. We introduce a comprehensive psychometrics benchmark for LLMs that covers six psychological dimensions: personality, values, emotion, theory of mind, motivation, and intelligence.
  arXiv  Detail & Related papers  (2024-06-25T16:09:08Z)
• 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)
• PHAnToM: Personality Has An Effect on Theory-of-Mind Reasoning in Large Language Models [25.657579792829743]
  This study investigates how inducing personalities in large language models with prompts affects their Theory-of-Mind (ToM) reasoning capabilities. We find that certain induced personalities can significantly affect the LLMs' reasoning capabilities in three different ToM tasks. We find that LLMs that exhibit a higher variance across personality prompts in ToM also tends to be more controllable in personality tests.
  arXiv  Detail & Related papers  (2024-03-04T17:34:34Z)
• Identifying Multiple Personalities in Large Language Models with External Evaluation [6.657168333238573]
  Large Language Models (LLMs) are integrated with human daily applications rapidly. Many recent studies quantify LLMs' personalities using self-assessment tests that are created for humans. Yet many critiques question the applicability and reliability of these self-assessment tests when applied to LLMs.
  arXiv  Detail & Related papers  (2024-02-22T18:57:20Z)
• Can Large Language Model Agents Simulate Human Trust Behaviors? [75.69583811834073]
  Large Language Model (LLM) agents have been increasingly adopted as simulation tools to model humans in applications such as social science. In this paper, we focus on one of the most critical behaviors in human interactions, trust, and aim to investigate whether or not LLM agents can simulate human trust behaviors.
  arXiv  Detail & Related papers  (2024-02-07T03:37:19Z)
• Illuminating the Black Box: A Psychometric Investigation into the Multifaceted Nature of Large Language Models [3.692410936160711]
  This study explores the idea of AI Personality or AInality suggesting that Large Language Models (LLMs) exhibit patterns similar to human personalities. Using projective tests, we uncover hidden aspects of LLM personalities that are not easily accessible through direct questioning. Our machine learning analysis revealed that LLMs exhibit distinct AInality traits and manifest diverse personality types, demonstrating dynamic shifts in response to external instructions.
  arXiv  Detail & Related papers  (2023-12-21T04:57:21Z)
• Do LLMs Possess a Personality? Making the MBTI Test an Amazing Evaluation for Large Language Models [2.918940961856197]
  We aim to investigate the feasibility of using the Myers-Briggs Type Indicator (MBTI), a widespread human personality assessment tool, as an evaluation metric for large language models (LLMs) Specifically, experiments will be conducted to explore: 1) the personality types of different LLMs, 2) the possibility of changing the personality types by prompt engineering, and 3) How does the training dataset affect the model's personality.
  arXiv  Detail & Related papers  (2023-07-30T09:34:35Z)
• Revisiting the Reliability of Psychological Scales on Large Language Models [66.31055885857062]
  This study aims to determine the reliability of applying personality assessments to Large Language Models (LLMs) By shedding light on the personalization of LLMs, our study endeavors to pave the way for future explorations in this field.
  arXiv  Detail & Related papers  (2023-05-31T15:03:28Z)
• 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.