| title | Parametric Social Identity Injection and Diversification in Public Opinion Simulation |
|---|---|
| authors | Hexi Wang, Yujia Zhou, Bangde Du, Qingyao Ai, Yiqun Liu |
| date | 2026-06-01 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2603.16142 |
| affiliation | Tsinghua University |
| upvotes | 1 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | 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. |
cite
@misc{wang2026parametric,
title={Parametric Social Identity Injection and Diversification in Public Opinion Simulation},
author={Hexi Wang and Yujia Zhou and Bangde Du and Qingyao Ai and Yiqun Liu},
year={2026},
eprint={2603.16142},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2603.16142}
}