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.

翻译：本文提出了一种数据集与概念框架，使大语言模型能够通过时间连续建模和上下文学习来模拟现实世界的情感动态，该框架利用物理信息神经网络，为可解释的对话建模开辟了新的可能性。