Exploring the Potential of Large Language Models to Simulate Personality

TL;DR

This paper investigates the ability of large language models to simulate human personality traits based on the Big Five model, highlighting current challenges and providing a dataset and framework for evaluation.

Contribution

It introduces a new dataset of personality-related texts and an analytical framework for testing LLMs' capability to simulate personality traits.

Findings

Generating personality traits remains challenging for LLMs.

The paper provides a benchmark dataset for personality simulation.

An analytical framework for evaluating personality simulation is proposed.

Abstract

With the advancement of large language models (LLMs), the focus in Conversational AI has shifted from merely generating coherent and relevant responses to tackling more complex challenges, such as personalizing dialogue systems. In an effort to enhance user engagement, chatbots are often designed to mimic human behaviour, responding within a defined emotional spectrum and aligning to a set of values. In this paper, we aim to simulate personal traits according to the Big Five model with the use of LLMs. Our research showed that generating personality-related texts is still a challenging task for the models. As a result, we present a dataset of generated texts with the predefined Big Five characteristics and provide an analytical framework for testing LLMs on a simulation of personality skills.