Building Social World Models with Large Language Models
Understanding and predicting how social beliefs evolve in response to events -- from policy changes to scientific breakthroughs -- remains a fundamental challenge in social science. Given LLMs' commonsense knowledge and social intelligence, we ask: Can LLMs model the dynamics of social beliefs following social events? In this work, we introduce the concept of the Social World Model (SWM), a general framework designed to capture how social beliefs evolve in response to major events. SWM learns state-transition functions for social beliefs by mining temporal patterns in social data and optimizing the evidence lower bound, without the need for explicit human annotations linking events to bel...
| Authors | Haofei Yu, Yining Zhao, Guanyu Lin et al. |
|---|---|
| Primary category | cs.SI |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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