Building Social World Models with Large Language Models
Summary
The paper introduces the Social World Model (SWM) framework, which uses large language models to model the dynamics of social beliefs in response to events, without explicit annotations. It also presents a benchmark SWM-bench derived from prediction markets and shows state-of-the-art results.
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Paper page - Building Social World Models with Large Language Models
Source: https://huggingface.co/papers/2606.11482
Abstract
Social World Model framework captures evolution of social beliefs in response to events through temporal pattern mining and evidence lower bound optimization without explicit human annotations.
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 theSocial World Model(SWM), a general framework designed to capture how social beliefs evolve in response to major events. SWM learnsstate-transition functionsfor social beliefs by miningtemporal patternsin social data and optimizing theevidence 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-worldprediction markets, specificallyKalshiandPolymarket. 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 onKalshidata and demonstrating competitive performance onPolymarketdata, while offering interpretable insights into the underlying mechanisms ofsocial belief dynamics.
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