# 参数化社会身份注入（PSII）：用于公众舆论模拟的多样性提升框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-01 08:00
- AIHOT 分数：54
- AIHOT 链接：https://aihot.virxact.com/items/cmq4oaypg006wslt2q1yyg5ze
- 原文链接：https://arxiv.org/abs/2603.16142

## AI 摘要

大语言模型（LLM）作为合成智能体进行公众舆论模拟时存在“多样性崩溃”问题——不同社会身份的表征在层间逐渐不可区分，导致响应同质化。为此提出参数化社会身份注入（PSII）框架，将人口统计属性与价值取向的显式参数化表示注入LLM中间隐藏状态，实现细粒度可控的身份调制。基于World Values Survey对多个开源LLM的实验显示，PSII显著提升了分布保真度与多样性，降低了与真实调查数据的KL散度。

## 正文

Large language models (LLMs) have recently been adopted as synthetic agents for public opinion simulation, offering a promising alternative to costly and slow human surveys. Despite their scalability, current LLM-based simulation methods fail to capture social diversity, producing flattened inter-group differences and overly homogeneous responses across demographic groups. We identify this limitation as a Diversity Collapse phenomenon in LLM hidden representations, where distinct social identities become increasingly indistinguishable across layers. Motivated by this observation, we propose Parametric Social Identity Injection (PSII), a general framework that injects explicit, parametric representations of demographic attributes and value orientations directly into intermediate hidden states of LLMs. Unlike prompt-based persona conditioning, PSII enables fine-grained and controllable identity modulation at the representation level. Extensive experiments on the World Values Survey using multiple open-source LLMs show that PSII significantly improves distributional fidelity and diversity, reducing KL divergence to real-world survey data while enhancing overall diversity. This work provides new insights into representation-level control of LLM agents and advances scalable, diversity-aware public opinion simulation.