BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data

• URL: http://arxiv.org/abs/2410.16491v1
• Date: Mon, 21 Oct 2024 20:32:27 GMT
• Title: BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data
• Authors: Wenkai Li, Jiarui Liu, Andy Liu, Xuhui Zhou, Mona Diab, Maarten Sap,
• Abstract summary: We introduce BIG5-CHAT, a large-scale dataset containing 100,000 dialogues designed to ground models in how humans express their personality in text. Our methods prompting outperform on personality assessments such as BFI and IPIP-NEO, with trait correlations more closely matching human data. Our experiments reveal that models trained to exhibit higher conscientiousness, higher agreeableness, lower extraversion, and lower neuroticism display better performance on reasoning tasks.
• Score: 28.900987544062257
• License:
• Abstract: In this work, we tackle the challenge of embedding realistic human personality traits into LLMs. Previous approaches have primarily focused on prompt-based methods that describe the behavior associated with the desired personality traits, suffering from realism and validity issues. To address these limitations, we introduce BIG5-CHAT, a large-scale dataset containing 100,000 dialogues designed to ground models in how humans express their personality in text. Leveraging this dataset, we explore Supervised Fine-Tuning and Direct Preference Optimization as training-based methods to align LLMs more naturally with human personality patterns. Our methods outperform prompting on personality assessments such as BFI and IPIP-NEO, with trait correlations more closely matching human data. Furthermore, our experiments reveal that models trained to exhibit higher conscientiousness, higher agreeableness, lower extraversion, and lower neuroticism display better performance on reasoning tasks, aligning with psychological findings on how these traits impact human cognitive performance. To our knowledge, this work is the first comprehensive study to demonstrate how training-based methods can shape LLM personalities through learning from real human behaviors.

Related papers

• 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)
• Personality Alignment of Large Language Models [26.071445846818914]
  Current methods for aligning large language models (LLMs) typically aim to reflect general human values and behaviors. We introduce the concept of Personality Alignment. This approach tailors LLMs' responses and decisions to match the specific preferences of individual users or closely related groups.
  arXiv  Detail & Related papers  (2024-08-21T17:09:00Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, integrating psychology-grounded principles of personality: social practice, consistency, and dynamic development. We incorporate personality traits directly into the model parameters, enhancing the model's resistance to induction, promoting consistency, and supporting the dynamic evolution of personality.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice [4.029252551781513]
  We propose a novel way to enhance the utility of Large Language Models as cognitive models. We show that an LLM pretrained on an ecologically valid arithmetic dataset, predicts human behavior better than many traditional cognitive models.
  arXiv  Detail & Related papers  (2024-05-29T17:37:14Z)
• 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)
• Aligning Large Language Models with Human: A Survey [53.6014921995006]
  Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect information. This survey presents a comprehensive overview of these alignment technologies, including the following aspects.
  arXiv  Detail & Related papers  (2023-07-24T17:44:58Z)
• Personality Traits in Large Language Models [44.908741466152215]
  Personality is a key factor determining the effectiveness of communication. We present a comprehensive method for administering and validating personality tests on widely-used large language models. We discuss application and ethical implications of the measurement and shaping method, in particular regarding responsible AI.
  arXiv  Detail & Related papers  (2023-07-01T00:58:51Z)
• Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
  We present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
  arXiv  Detail & Related papers  (2020-07-07T17:19:56Z)

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.