Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLM s (2025)

Abstract: Previous approaches to persona simulation large language models (LLMs) have typically relied on learning basic biographical information, or using limited role-play dialogue datasets to capture a character’s responses. However, a holistic representation of an individual goes beyond surface-level facts or conversations to deeper thoughts and thinking. In this work, we introduce CharacterBot, a model designed to replicate both the linguistic patterns and distinctive thought patterns as manifested in the textual works of a character. Using Lu Xun, a renowned Chinese writer as a case study, we propose four training tasks derived from his 17 essay collections. These include a pre-training task focused on mastering external linguistic structures and knowledge, as well as three fine-tuning tasks: multiple-choice question answering, generative question answering, and style transfer, each aligning the LLM with Lu Xun’s internal ideation and writing style. To optimize learning across these tasks, we introduce a CharLoRA parameter updating mechanism, where a general linguistic style expert collaborates with other task-specific experts to better study both the language style and the understanding of deeper thoughts. We evaluate CharacterBot on three tasks for linguistic accuracy and opinion comprehension, demonstrating that it significantly outperforms the baselines on our adapted metrics. We hope this work inspires future research on deep character persona simulation LLMs: https://github.com/zxwang63/characterbot

## introduction developing language models that simulate character personas by embodying specific individuals' personalities and worldviews has been a longstanding objective in nlp. researchers in this area have explored various approaches. some models * corresponding author. ## related work character persona simulation. character persona simulation refers to the task of assigning personality traits to virtual agents in order to facilitate realistic and contextually coherent interactions @xcite . in recent years, this task has received widespread attention, particularly in approaches leveraging llms. with their strong capabilities in natural language understanding and generation @xcite @xcite , llms have become a key tool for simulating diverse personas. currently, two main approaches are employed: prompt-based methods and fine-tuningstyle transfer. style transfer for text involves altering the stylistic attributes of a source text while preserving its core meaning @xcite . @xcite introduce an augmented zero-shot learning method, which leverages llms to achieve versatile text-style transformations without requiring task-specific training. @xcite evaluate the performance of chatgpt in sentence style transfer by comparing chatgpt generated texts with those created by humans. @xcite explore a novel approach to style transfer by integrating the chain-of-thought reasoning capabilities of llms with knowledge distillation techniques. however, these methods predominantly focus on linguistic style without engaging with deeper ideological dimensions. parameter-efficient fine-tuning. parameter-efficient fine-tuning (peft) techniques are crucial for adapting large pre-trained models efficiently, especially in the context of llms @xcite @xcite . prefix tuning optimizes learnable prefixes added to input embeddings or intermediate representations @xcite , while prompt tuning adjusts learnable prompt embeddings to guide downstream task adaptation @xcite . among peft methods, lora is widely used, introducing lowrank matrices to fine-tune models efficiently @xcite . its variant, qlora, further improves efficiency through quantization @xcite . recent innovations have improved lora's domain-specific adaptability. @xcite propose a dynamic mechanism for retrieving and combining lora modules in mixed task scenarios. @xcite enable efficient personalized llms by sharing partial peft parameters across users, and @xcite introduce a flexible lora merging strategy suited for multi-task learning without additional training. in our work, we adapt lora for deep persona simulation. ## problem formulation we formally define the task of deep persona simulation as the generation of character-specific responses and stylistic outputs that align with the inferred cognitive and linguistic patterns of a target individual, primarily derived from their textual corpus. the depth refers to capturing nuances beyond surface-level facts, aiming for a consistent representation of style, tone, and recurring themes, rather than an ontological claim about replicating an inner self. a single model m is designed to perform multiple tasks, which together assess its ability to understand and generate character-specific responses. the first task is multiple-choice question answering, where the model m is given a question q and a set of candidate answers {a 1 , a 2 , . . . , a n }. the model predicts the correct answer a * based on its understanding of the character's perspective: the second task is generative question answering, where the model m generates an appropriate answer a given a question q, reflecting the character's knowledge, opinions, or personality: the final task is style transfer, where the model m rewrites an input text t input into an output text t output that aligns with a specified persona style s: t @xmath0, s; θ). ## method as shown in figure 2 , our characterbot is first pretrained and then fine-tuned on three downstream tasks. in the following sections, we introduce the task-specific objectives and adapted training datasets for each stage, followed by a detailed explanation of our customized charlora. ## conclusion in this paper, we present characterbot, a model that simulates both the linguistic patterns and deeper thought processes of a character. using lu xun as a case study, we adopt a multi-task approach, combining pre-training on linguistic structures with fine-tuning through tasks like question answering and style transfer. we introduce the charlora updating mechanism, enabling collaboration between general and task-specific experts. experimental results show that characterbot outperforms baselines in linguistic accuracy and opinion comprehension. in future work, we aim to extend it to diverse personas and multi-layered character simulations, including a broader range of authors and genres.