Restoring Heterogeneity in LLM-based Social Simulation: An Audience Segmentation Approach
Large Language Models (LLMs) are increasingly used to simulate social attitudes and behaviors, offering scalable "silicon samples" that can approximate human data. However, current simulation practice often collapses diversity into an "average persona," masking subgroup variation that is central to social reality. This study introduces audience segmentation as a systematic approach for restoring heterogeneity in LLM-based social simulation. Using U.S. climate-opinion survey data, we compare six segmentation configurations across two open-weight LLMs (Llama 3.1-70B and Mixtral 8x22B), varying segmentation identifier granularity, parsimony, and selection logic (theory-driven, data-driven, a...
| Authors | Xiaoyou Qin, Zhihong Li, Xiaoxiao Cheng |
|---|---|
| Primary category | cs.CY |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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