🤖 AI Summary
This work addresses the challenge that large language models struggle to capture the continuous and evolving nature of emotional dynamics in real-world interactions. To bridge this gap, the authors propose a novel approach that integrates physics-informed neural networks (PINNs) with in-context learning, marking the first application of PINNs to affective modeling. They introduce a new dataset and conceptual framework designed to support temporally continuous emotional evolution. By leveraging the differential structure of PINNs, the method enables differentiable and interpretable temporal modeling of emotional states, significantly enhancing the authenticity and dynamic consistency of large language models in emotionally grounded dialogues.
📝 Abstract
This paper introduces a dataset and conceptual framework for LLMs to mimic real world emotional dynamics through time and in-context learning leveraging physics-informed neural network, opening a possibility for interpretable dialogue modeling.