Enabling Trait-based Personality Simulation in Conversational LLM Agents: Case Study of Customer Assistance in F rench (2025)

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.

## introduction the human personality is a rich and complex construct that deeply influences communication and interaction in various contexts. to better understand and model personality, psychologists have developed numerous frameworks, with the big five @xcite personality model emerging as one of the most robust and widely accepted -see @xcite for a detailed survey. it includes five dimensions, openness, conscientiousness, extraversion, agreeableness, and neuroticism (ocean), which provide a comprehensive representation of personality. in the field of natural language processing (nlp), the integration of personality into chatbots has garnered increasing attention. personalised chatbots aim to provide more engaging and contextually appropriate interactions by embodying different personality traits. however, existing approaches that use the big five model often fail to adequately represent the complexity of human personality. some methods define the personality of the chatbot using a single dimension of @xcite , while others employ binary combinations of traits @xcite , producing a limited set of possibilities (2 5 = 32) that may not reflect the nuanced of personalities in the real world. to address these limitations, we propose a novel approach to personality modelling for chatbots. our method utilises a [0,1]-continuous 5dimensional vector, where each coordinate represents the degree of presence for a given ocean trait, allowing for more granular and flexible personality profiles. this vector-based representation is designed to steer the generative output of large language models (llms) depending on predefined persona descriptions, allowing for a more dynamic implementation of personality. we applied this methodology in the context of customer assistance in french, using an instructionfollowing llm as the base chatbot. building on previous researches @xcite @xcite , we opted to influence chatbot behaviour by mapping personality vectors into prompts used for incontext learning, rather than directly modifying the model's weights. this approach is extremely less costly, but more importantly avoids the forgetting and capability reduction issues generally associated with weight modifications on the scale of large pretrained lm. by steering chatbot behaviour through this vector mapping-into-prompt-based method, we aimed to address the following research questions (rqs): the big five model is a psychological framework that categorizes personality traits into five main dimensions. also referred to as the ocean model, based on those dimensions: openness (o) characterized by originality, curiosity, and ingenuity; conscientiousness (c) characterized by orderliness, responsibility, and dependability; extraversion (e) characterized by talkativeness, assertiveness, and energy; agreeableness (a) characterized by good-naturedness, cooperativeness, and trust; neuroticism (n) characterized by upsetability and is the polar opposite of emotional stability. fixed context prompt: about customer assistance in french personality enforcement persona information user: <@xmath0: <@xmath1: <@xmath2: <@xmath3: <@xmath4: neurotism extraversion llm stored personas rulebased vecto r to text trans form s am p le a p er so n a openness prompt builder dialogue history personality evaluation & impact on efficiency personality vector prediction 1 2 conversation quality and user experience assessment five-dimension personality hypercube* llm * for representation purpose the 5-hypercube is limited to a cube big five persoanlity model incorporation in llm-based chatbots ocean vector: a personality is represented as a five-dimensional vector, with each dimension corresponding to an ocean trait ranging from 0 (absent) to 1 (highly expressed), capturing diverse personality traits. this vector is then used to constrain the personality exhibited by the llm-based agent. following prior research @xcite , in-context learning (icl) was selected as the integration method due to its effectiveness in leveraging state-of-the-art instruction-following llms. unlike fine-tuning, which can degrade performance in low-data scenarios, icl preserves the model's adaptability without requiring additional training or data. ## experimental setup the proposed approach is evaluated, in the case study of a customer service. the full spectrum of personality traits is reduced to three "polarities" based on assumed desirable and undesirable traits for customer interactions following the ocean acronym: two extremes [1, 1, 1, 1, 0] vs [0, 0, 0, 0, 1] and one intermediate [0.5, 0.6, 0.5, 0.5, 0.4] referred to as the "good", the "bad" and the "moderate". the complete experimental setup is illustrated in figure 1 . ## conclusion this study proposed a five-dimensional vector to represent personality traits, which was incorporated into an llm through icl. both humans and llms were able to distinguish personality polarities effectively, with low mses and observable clusters. the ṽp strongly correlated with quality criteria, showing that personality influenced user experience and agent efficiency.