Exploring the Potential of Large Language Models to Simulate Personality
This work addresses the problem of enhancing user engagement in conversational AI by personalizing chatbots, but it is incremental as it builds on existing LLM capabilities without a major breakthrough.
The paper tackled the challenge of simulating personality traits using large language models (LLMs) based on the Big Five model, resulting in the creation of a dataset of generated texts and an analytical framework for testing LLMs on personality simulation, though it found the task remains difficult.
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.