Explainable Multimodal Emotion Classification with Chain-of-Thought Prompting
We will leverage Multimodal Large Language Models (MLLMs) to bridge the gap between "black-box" classification and human-like emotional reasoning. Specifically, we will evaluate Chain-of-Thought prompting for emotion estimation during human-to-human interaction to investigate reasoning traces and jointly evaluate motion and affect dynamics.




