P ersona T win: A Multi-Tier Prompt Conditioning Framework for Generating and Evaluating Personalized Digital Twins (2025)

Abstract: While large language models (LLMs) afford new possibilities for user modeling and approximation of human behaviors, they often fail to capture the multidimensional nuances of individual users. In this work, we introduce \texttt{PersonaTwin} , a multi-tier prompt conditioning framework that builds adaptive digital twins by integrating demographic, behavioral, and psychometric data. Using a comprehensive data set in the healthcare context of more than 8,500 individuals, we systematically benchmark \texttt{PersonaTwin} against standard LLM outputs, and our rigorous evaluation unites state-of-the-art text similarity metrics with dedicated demographic parity assessments, ensuring that generated responses remain accurate and unbiased. Experimental results show that our framework produces simulation fidelity on par with oracle settings. Moreover, downstream models trained on persona-twins approximate models trained on individuals in terms of prediction and fairness metrics across both GPT-4o-based and Llama-based models. Together, these findings underscore the potential for LLM digital twin-based approaches in producing realistic and emotionally nuanced user simulations, offering a powerful tool for personalized digital user modeling and behavior analysis.

## introduction large language models (llms) present exciting opportunities for user modeling, behavior analysis, and understanding and improving the human condition. opportunities abound across an array of contexts including healthcare, education, etc. for instance, a pressing healthcare challenge is the development of conversational systems that truly account for the nuanced experiences and identities of individual patients @xcite . in telemedicine or mental health coaching scenarios, clinicians require tools that adapt dynamically to each patient's demographic, behavioral, and psychological profile, rather than offering generic responses. although large language models such as gpt-4 @xcite and llama-3-70b @xcite have shown substantial improvement in natural language processing tasks-demonstrated by benchmarks in medical qa datasets, automated note taking, and patient triage use cases-they still struggle to model the multifaceted nature of personal identity in real world settings @xcite . numerous persona-based conversational frameworks have begun to address this gap by incorporating basic user attributes into language models, and studies have demonstrated modest gains in engagement and trust when even minimal demographic cues are included @xcite . however, many of these frameworks remain limited by static or simplistic representations that fail to capture evolving factors. in the health setting, for instance, these frameworks fail to represent health behaviors over time, emotional states during stressful events, and shifting attitudes toward medical professionals @xcite . to overcome these limitations, we draw on the concept of digital twins, originally popularized in engineering, to represent physical systems virtually @xcite . in our adaptation, a "digital twin" for conversational ai is a virtual replica of a user (e.g., a patient) that encapsulates not only demographic information (e.g., age, gender, and socioeconomic status), but also behavioral data (e.g., physical activity, dialogue habits, and compliance with medications), along with psychological attributes (e.g., anxiety levels, trust, and perceived literacy) @xcite . this framework is particularly relevant in scenarios such as mental health chatbots or chronic disease management systems, where the emotional and psychological realism of the dialogue can directly impact patient adherence and satisfaction. however, creating such multidimensional and adaptive representations raises several methodolog-ical hurdles. first, many existing language-based approaches provide only a narrow view of a user's identity, focusing predominantly on stylistic or linguistic features while neglecting deeper demographic or psychometric attributes. second, static systems do not adapt to shifting contexts, including new symptoms or a gradual erosion of trust, resulting in repetitive or misaligned conversations. third, there is a lack of comprehensive evaluation benchmarks that jointly measure factual correctness, emotional coherence, and alignment with actual user expressions. for instance, in clinical contexts, much of the previous nlp work has focused on factual accuracy, leaving emotional nuance and user alignment underexplored @xcite . to address these challenges, we introduce personatwin, a multi-tier prompt conditioning framework that systematically integrates demographic, behavioral, and psychological data into a comprehensive digital twin. our approach employs a structured methodology in which each level of user information is processed and encoded into the model prompt @xcite . personatwin consists of two parts, multitiered conditioning for digital twin creation and conversation update loop. in the first part, step 1 involves mapping person-level persona metadata to persona information tiers such as demographics, behavioral, and psychological information; whereas step 2 initializes the digital twin. in the second part, the instantiated digital twin is iteratively updated with the real person's previous conversational responses to psychometric questions (e.g., related to numeracy, anxiety, etc.). this layered technique enables the model to produce simulated dialogues that are not only contextually relevant but also capable of reflecting shifting user states as new data are introduced @xcite . we tested our framework using a large-scale psychometric dataset of more than 8,500 respondents @xcite , which provides a rich combination of survey-based measures, user-generated text, and demographic information. by incorporating real responses on health numeracy, medical visit anxiety, and trust in healthcare providers, we ensure that our simulations reflect authentic user experiences while maintaining privacy through deidentification and ethical safeguards @xcite . to rigorously evaluate personatwin, we implemented a dual-pronged strategy. first, we em-ploy state-of-the-art text similarity metrics to measure how closely the digital twin-generated output matches the actual user responses @xcite @xcite . second, we use a downstream nlp prediction task to examine the efficacy of the generated twins, relative to the actual users, in terms of the fine-tuned model's predictive performance and fairness assessments across key demographic dimensions @xcite . our key contributions are: (i) we introduce personatwin, a multi-tier framework that integrates demographic, behavioral, and psychological data to generate adaptive digital twins, enhancing realism with llm-driven personal insights, (ii) we generate 8,500+ digital twins representing diverse personas and validate response fidelity using conditioned experiments and advanced similarity metrics, and (iii) we conduct a rigorous downstream evaluation of models trained/tested on generated personas versus actual users and show that the persona-based models achieve comparable predictive power and fairness outcomes. foot_0 2 related work our work addresses this challenge by taking a fine-grained, high-dimensional approach to simulating individual personas. we integrate psychological, behavioral, personal background, and linguistic style information to construct digital twins that capture the nuanced and evolving nature of real human identities. by leveraging authentic user inputs as benchmarks, our framework explores replication of core behavioral patterns and individual variability that is typically lost in more simplistic, one-dimensional models. we demonstrate the potential for enriched representations for generating digital twins that better reflect real human behavior. ## methodology in this section, we introduce the structure of our proposed framework, personatwin ( §3.1), and then detailed our evaluation metrics ( §3.2). ## conclusion we present personatwin, a multi-tier prompt conditioning framework that enhances digital twin realism and fairness as demonstrated in a healthcare ai context. by combining structured persona encoding with iterative refinement, personatwin generates context-aware responses with competitive downstream performance and fairness potential for fine-tuned nlp models relative to true responses. extensive evaluations on 8,500 individuals demonstrate significant improvements in simulation fidelity, and maintaining fairness with demographic parity indices consistently ranging between 0.87 and 1.01 across different model architectures. future directions include expanding psychometric dimensions and enabling real-time adaptation for more accurate downstream predictive power.