利用大语言模型构建社会世界模型

摘要

理解并预测社会信念如何因应事件（从政策变化到科学突破）而演变，仍是社会科学领域的核心挑战。鉴于大语言模型具备常识性知识和社会智能，我们提出疑问：大语言模型能否模拟社会事件后的信念动态？本研究引入社会世界模型这一概念，构建了捕捉重大事件后社会信念演变规律的通用框架。该模型通过挖掘社会数据中的时间模式并优化证据下界，学习社会信念的状态转移函数，无需人工标注事件与信念变化之间的关联，也无需昂贵的普查数据。为评估社会世界模型，我们基于现实预测市场（特别是Kalshi和Polymarket）构建了SWM-bench基准测试集。该基准包含逾1.2万个数据点，覆盖政治、金融和加密货币等多领域的社会信念预测任务。实验结果表明，社会世界模型显著优于时序基础模型，在Kalshi数据集上取得最先进性能，在Polymarket数据集上展现竞争力，同时为社会信念动态的潜在机制提供了可解释性洞见。

English

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 belief shifts, or for expensive census data. To evaluate SWM, we introduce a benchmark, SWM-bench, derived from real-world prediction markets, specifically Kalshi and Polymarket. SWM-bench includes over 12k data points for social belief prediction tasks spanning diverse domains such as politics, finance, and cryptocurrency. Our experimental results show that SWM significantly outperforms time-series foundation models, achieving state-of-the-art results on Kalshi data and demonstrating competitive performance on Polymarket data, while offering interpretable insights into the underlying mechanisms of social belief dynamics.