@inproceedings{njifenjou-etal-2025-enabling,
    title = "Enabling Trait-based Personality Simulation in Conversational {LLM} Agents: Case Study of Customer Assistance in {F}rench",
    author = "Njifenjou, Ahmed  and
      Sucal, Virgile  and
      Jabaian, Bassam  and
      Lef{\`e}vre, Fabrice",
    editor = "Torres, Maria Ines  and
      Matsuda, Yuki  and
      Callejas, Zoraida  and
      del Pozo, Arantza  and
      D'Haro, Luis Fernando",
    booktitle = "Proceedings of the 15th International Workshop on Spoken Dialogue Systems Technology",
    month = may,
    year = "2025",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.iwsds-1.32/",
    pages = "299--308",
    ISBN = "979-8-89176-248-0",
    abstract = "Among the numerous models developed to represent the multifaceted complexity of human personality, particularly in psychology, the Big Five (commonly referred to as `OCEAN', an acronym of its five traits) stands out as a widely used framework. Although personalized chatbots have incorporated this model, existing approaches, such as focusing on individual traits or binary combinations, may not capture the full diversity of human personality. In this study, we propose a five-dimensional vector representation, where each axis corresponds to the degree of presence of an OCEAN trait on a continuous scale from 0 to 1. This representation is designed to enable greater versatility in modeling personality. Application to customer assistance scenarios in French demonstrates that, based on humans-bots as well as bots-bots conversations, assigned personality vectors are distinguishable by both humans and LLMs acting as judges. Both of their subjective evaluations also confirm the measurable impacts of the assigned personality on user experience, agent efficiency, and conversation quality."
}