arXiv cs.AI· Zhengyu Hu, Jianxun Lian, Zheyuan Xiao, Max Xiong, Tianfu Wang, Teng Xiao, Yuxuan Lei, Fengqing Jiang, Kaize Ding, Ziang Xiao, Nicholas Jing Yuan, Xing Xie, Radha Poovendran·· 4 小时前AI 评分33
面向大语言模型社会仿真的人群对齐人格生成框架（arXiv 论文）
Population-Aligned Persona Generation for LLM-based Social Simulation
AI 导读
研究者提出一个为 LLM 社会仿真合成人群对齐人格集的系统性框架：先从长期社交媒体数据生成叙事化 persona，经质量评估过滤低保真档案，再用重要性采样与 Big Five 人格特质等心理测量分布做全局对齐，并引入任务特定模块适配目标子人群。实验显示该方法显著降低人群层面偏差，可用于广泛的研究与政策仿真场景。
正文 · 原文
Authors:Zhengyu Hu, Jianxun Lian, Zheyuan Xiao, Max Xiong, Tianfu Wang, Teng Xiao, Yuxuan Lei, Fengqing Jiang, Kaize Ding, Ziang Xiao, Nicholas Jing Yuan, Xing Xie, Radha Poovendran
Abstract:Recent advances in large language models (LLMs) have enabled large-scale, high-fidelity social simulations, creating new opportunities for computational social science. However, constructing persona sets that faithfully reflect real-world population diversity remains a key challenge. Existing studies often emphasize agentic frameworks and simulation environments, while paying less attention to persona generation and the biases introduced by unrepresentative persona sets. In this paper, we propose a systematic framework for synthesizing high-quality, population-aligned persona sets for LLM-driven social simulation. Our approach begins by leveraging LLMs to generate narrative personas from long-term social media data, followed by rigorous quality assessment to filter out low-fidelity profiles. We then apply importance sampling to achieve global alignment with reference psychometric distributions, such as the Big Five personality traits. To address the needs of specific simulation contexts, we further introduce a task-specific module that adapts the globally aligned persona set to targeted subpopulations. Extensive experiments demonstrate that our method significantly reduces population-level bias and enables accurate, flexible social simulation for a wide range of research and policy applications.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2509.10127 [cs.CL] |
| (or arXiv:2509.10127v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2509.10127 arXiv-issued DOI via DataCite |
Submission history
From: Zhengyu Hu [view email]
[v1]
Fri, 12 Sep 2025 10:43:47 UTC (1,711 KB)
[v2]
Sat, 4 Oct 2025 09:04:38 UTC (1,711 KB)
[v3]
Wed, 4 Nov 2026 09:22:24 UTC (2,031 KB)
来源：arXiv cs.AI · arxiv.org