P ers LLM : A Personified Training Approach for Large Language Models (2025)

Abstract: Large language models (LLMs) exhibit human-like intelligence, enabling them to simulate human behavior and support various applications that require both humanized communication and extensive knowledge reserves. Efforts are made to personify LLMs with special training data or hand-crafted prompts, while correspondingly faced with challenges such as insufficient data usage or rigid behavior patterns. Consequently, personified LLMs fail to capture personified knowledge or express persistent opinion. To fully unlock the potential of LLM personification, we propose PersLLM, a framework for better data construction and model tuning. For insufficient data usage, we incorporate strategies such as Chain-of-Thought prompting and anti-induction, improving the quality of data construction and capturing the personality experiences, knowledge, and thoughts more comprehensively. For rigid behavior patterns, we design the tuning process and introduce automated DPO to enhance the specificity and dynamism of the models’ personalities, which leads to a more natural opinion communication. Both automated metrics and expert human evaluations demonstrate the effectiveness of our approach. Case studies in human-machine interactions and multi-agent systems further suggest potential application scenarios and future directions for LLM personification.

## introduction large language models (llms) have demonstrated human-level intelligence in multiple domains due to extensive parameters and data @xcite . this has driven research into using llms as human-like agents in social simulations, human-machine interactions, and multi-agent systems @xcite @xcite . aligning agents with specific personalities enhances user comfort, improves knowledge mastery and facilitates collaboration @xcite , making personification crucial for applications such as online education, consultation and public opinion analysis. efforts to integrate personalities into llms typically follow two approaches. training-based methods embed personality traits into model parameters using targeted data @xcite . unfortunately, the lack of comprehensive and theoretical analysis in data construction has led to insufficient use of raw materials, focusing only on modeling isolated features such as language style or anecdotes. meanwhile, prompt-based methods rely on prompt engineering to define traits @xcite , offering flexible functions activated by hand-crafted prompts for both data annotation and personified inference. however, their effectiveness is limited by the model's rigid behavior patterns, often leading to issues like over-accommodation to user inputs. inspired by these concerns, we propose per-sllm, a comprehensive approach to llm personification including personified data construction and model tuning. to achieve a sufficient data usage, we use a widely adopted conversational data format annotated by advanced llms in retrievalaugmented generation (rag) @xcite . the processes for retrieving raw data, formulating inputs, and generating responses are outlined, with strategies such as material classification, antiinduction, and chain-of-thought (cot) @xcite prompting employed. to avoid the rigid behavior patterns of llms, we integrate personified data with general instruction tuning data to fine-tune the model and then apply direct preference optimization (dpo) @xcite to highlight personality and temporal differences. to evaluate persllm, we examine it from both theoretical and practical perspectives. theoretically, psychologists define personality as a dynamic organization of psychophysical systems that generates consistent patterns of behavior, thought, and feeling @xcite . based on this, we establish three key criteria for llm personification: distinction, consistency, and dynamic development, which we assess through quantitative experiments and psychological scale measurements. practically, we evaluate humanized knowledge and opinion interactions by examining the precision of the generation and the performance of the human-agent interaction. experimental results show that per-sllm achieves more precise alignment with specific personalities and superior interaction performance in real-world scenarios. our contributions are as follows: (1) we design persllm, a pioneering approach to llm personification, and provide a detailed comparison of data construction and model tuning. (2) our experiments validate the method's effectiveness in enhancing human interaction simulations and improving human-agent communication quality. (3) we make the code for data collection, model training, and evaluation publicly available, along with demonstrations to support further research and academic discussions on personified ai systems. ## related work llms demonstrate emergent abilities @xcite like in-context learning and cot reasoning. to better align models with human intentions, alignment technologies @xcite emerged. from a goal perspective, people apply some sociological methods to find human consensus and design values that ai should conform to @xcite , and sometimes also focus on domain knowledge @xcite instead of social value. from a data perspective, alignment training usually adopts human feedback @xcite or strong-llm-annotated data @xcite . from a methodology perspective, researchers iteratively design alignment algorithms including proximal policy optimization (ppo) @xcite , dpo @xcite , and odds ratio preference optimization (orpo) @xcite , using human preference data to improve model performances. in addition, some studies @xcite have examined alignment methods for personalization, but their tasks and settings differ and do not involve persona-grounded scenarios. in our work, we align models to specific personalities, incorporating personal values and knowledge with llm-annotated conversations and automatically pairing preference data. research on llm personification is limited but emerging. llms have demonstrated the ability to express personality traits in terms of psychological characteristics @xcite . they may have great potential in fields including education @xcite and healthcare @xcite . by designing special prompts, llms can basically achieve role-play and simulate dialogue-agent behavior @xcite , enhancing the creativity of group discussion @xcite . explicit supervision from predefined belief networks @xcite or instant human-crafted principles @xcite can further improving the human-like actions of llms. to construct large-scale training data for llm personification, formats including drama scripts @xcite @xcite and experience scene @xcite @xcite are adopted to help llms better simulate target personalities in practical scenarios. further, it is also practical to generate conversational personified training data by editing existing datasets to specific character styles and knowledge scope @xcite . some studies also focus on evaluating llms in role-playing tasks through personality traits @xcite @xcite and interactive evaluation @xcite ; however, the evaluation criteria have not yet been widely adopted. our evaluation adopts a more comprehensive approach, encompassing psychology-based assessments grounded in the big five personality traits, objective performance metrics, and human evaluations.