Social Processes: Probabilistic Meta-learning for Adaptive Multiparty Interaction Forecasting

• URL: http://arxiv.org/abs/2501.01915v1
• Date: Fri, 03 Jan 2025 17:34:53 GMT
• Title: Social Processes: Probabilistic Meta-learning for Adaptive Multiparty Interaction Forecasting
• Authors: Augustinas Jučas, Chirag Raman,
• Abstract summary: We introduce Social Process (SP) models, which predict a distribution over future multimodal cues jointly for all group members. We also analyze the generalization capabilities of SP models in both their outputs and latent spaces through the use of realistic synthetic datasets.
• Score: 3.9134031118910264
• License:
• Abstract: Adaptively forecasting human behavior in social settings is an important step toward achieving Artificial General Intelligence. Most existing research in social forecasting has focused either on unfocused interactions, such as pedestrian trajectory prediction, or on monadic and dyadic behavior forecasting. In contrast, social psychology emphasizes the importance of group interactions for understanding complex social dynamics. This creates a gap that we address in this paper: forecasting social interactions at the group (conversation) level. Additionally, it is important for a forecasting model to be able to adapt to groups unseen at train time, as even the same individual behaves differently across different groups. This highlights the need for a forecasting model to explicitly account for each group's unique dynamics. To achieve this, we adopt a meta-learning approach to human behavior forecasting, treating every group as a separate meta-learning task. As a result, our method conditions its predictions on the specific behaviors within the group, leading to generalization to unseen groups. Specifically, we introduce Social Process (SP) models, which predict a distribution over future multimodal cues jointly for all group members based on their preceding low-level multimodal cues, while incorporating other past sequences of the same group's interactions. In this work we also analyze the generalization capabilities of SP models in both their outputs and latent spaces through the use of realistic synthetic datasets.

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