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After the inception of emotion recognition or affective computing, it hasincreasingly become an active research topic due to its broad applications.Over the past couple of decades, emotion recognition models have graduallymigrated from statistically shallow models to neural network-based deep models,which can significantly boost the performance of emotion recognition models andconsistently achieve the best results on different benchmarks. Therefore, inrecent years, deep models have always been considered the first option foremotion recognition. However, the debut of large language models (LLMs), suchas ChatGPT, has remarkably astonished the world due to their emergedcapabilities of zero/few-shot learning, in-context learning, chain-of-thought,and others that are never shown in previous deep models. In the present paper,we comprehensively investigate how the LLMs perform in emotion recognition interms of diverse aspects, including in-context learning, few-short learning,accuracy, generalisation, and explanation. Moreover, we offer some insights andpose other potential challenges, hoping to ignite broader discussions aboutenhancing emotion recognition in the new era of advanced and generalised largemodels.

Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models