This study proposes a unified theoretical framework called "generative emergent communication" (generative EmCom) that connects emergent communication, world models, and large language models from the perspective of collective predictive coding. This framework not only allows us to understand symbol emergence as (decentralized) representation learning of external symbols within a machine learning framework but also enables us to interpret large language models as collective world models that integrate experiences from multiple agents. This research provides a novel perspective linking world models and symbol emergence, while discussing future research directions.