參數化社會認同注入與多樣化於輿論模擬

摘要

大语言模型（LLMs）近期被用作舆论模拟的合成代理，为成本高昂且进程缓慢的人工调查提供了有前景的替代方案。尽管具备可扩展性，当前基于LLM的模拟方法仍难以捕捉社会多样性，导致群体间差异扁平化，不同人口群体的回应同质化严重。我们将这一局限识别为LLM隐藏表征中的"多样性崩溃"现象——不同社会身份特征随网络层数加深而逐渐难以区分。基于该发现，我们提出参数化社会身份注入（PSII）框架，该通用方法将人口属性与价值取向的显式参数化表征直接注入LLM的中间隐藏状态。不同于基于提示的人格条件设定，PSII可在表征层面实现细粒度、可控的身份调节。在多个开源LLM上的世界价值观调查实验中，PSII显著提升了分布保真度与多样性，在降低与现实调查数据KL散度的同时增强了整体多样性。本研究为LLM代理的表征级控制提供了新视角，推动可扩展且具有多样性意识的舆论模拟发展。

English

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.