[1]柴春雷,葛智超,殷敏,等.大语言模型人格化表达实现技术综述[J].智能系统学报,2026,21(2):321-336.[doi:10.11992/tis.202505031]
CHAI Chunlei,GE Zhichao,YIN Min,et al.A survey of techniques for realizing personality expression in large language models[J].CAAI Transactions on Intelligent Systems,2026,21(2):321-336.[doi:10.11992/tis.202505031]
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CHAI Chunlei,GE Zhichao,YIN Min,et al.A survey of techniques for realizing personality expression in large language models[J].CAAI Transactions on Intelligent Systems,2026,21(2):321-336.[doi:10.11992/tis.202505031]
大语言模型人格化表达实现技术综述
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
21
期数:
2026年第2期
页码:
321-336
栏目:
综述
出版日期:
2026-03-05
- Title:
- A survey of techniques for realizing personality expression in large language models
- Keywords:
- large language models; personality expression in large language models; personality assessment; personality generation; digital persona; natural language processing; role-playing; personality alignment
- 分类号:
- TP18
- 摘要:
- 本文对大语言模型(large language models, LLMs)人格化表达的实现技术进行了系统性综述。文章首先回顾了早期模型通过词向量和句嵌入等拟人化文本表达的发展历程。随着大语言模型的普及和技术成熟，模型已能够根据不同场景和任务需求，呈现出具有特定角色设定的人格特征。当前实现大语言模型人格化表达的技术路径主要包括3个层面：模型内部的预训练数据优化、微调，模型外部的提示词、强化学习、智能体工作流设计，以及从评估层面对人格表达进行对齐。这些技术既可通过调整模型内部参数，实现人格化定制，也可在不修改核心参数的前提下，通过外部机制实现人格化表达的灵活调控。最后，基于当前模型人格化生成技术的发展现状，本文对该领域的技术发展趋势和应用前景进行了分析与展望。
- Abstract:
- Personality expression in Large Language Models (LLMs) has emerged as a key direction in human–computer interaction research. Enabling machines to exhibit uniquely human-like expressiveness remains a significant challenge in the LLM domain. In recent years, the application of pre-training, fine-tuning, and agent-based collaboration techniques has matured to the point where LLMs can adopt role-specific personalities tailored to diverse scenario and task requirements. Whether through carefully curated training datasets at the input stage, parameter-level adaptations within the model, or external agents and workflows around the model, personality expression in LLMs can be achieved. This paper provides a comprehensive review of the current state and future trends of personality expression in LLMs. On one hand, it examines techniques for extracting and simulating personality traits; on the other, it explores methods for controlling and aligning model personality. By analyzing and summarizing these approaches, we discuss the developmental directions of personality-driven LLM research.
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备注/Memo
收稿日期:2025-5-31。
作者简介:柴春雷，教授，博士生导师，浙江大学现代工业设计研究所副所长，中国机械工程学会工业设计分会常务委员兼总干事，中国好设计商业模式评审组组长。主要研究方向为文化创新设计、商业创新设计、智能设计。主持国家社科基金艺术学项目、中国工程院重大咨询项目子课题、国家自然科学基金项目。发表学术论文20余篇，出版著作5部、教材1部。E-mail：dishengchai@126.com。;葛智超，博士研究生，主要研究方向为自然语言处理。E-mail：gezhichao@zju.edu.cn。;殷敏，特聘副研究员，主要研究方向为社媒数据与用户画像、数字人格、人格计算。主持2024年教育部海外博士后引才专项资助项目。E-mail：amyin@zju.edu.cn。
通讯作者:殷敏. E-mail：amyin@zju.edu.cn
更新日期/Last Update:
1900-01-01