Parametric Social Identity Injection and Diversification in Public Opinion Simulation

Wang Hexi, Yujia Zhou, Bangde Du, Qingyao Ai, Yiqun Liu

Parametric Social Identity Injection and Diversification in Public Opinion Simulation: 1 upvotes on Hugging Face Daily Papers, #39 of 48 papers on 2026-06-08. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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