  After the inception of emotion recognition or affective computing, it has
increasingly become an active research topic due to its broad applications.
Over the past couple of decades, emotion recognition models have gradually
migrated from statistically shallow models to neural network-based deep models,
which can significantly boost the performance of emotion recognition models and
consistently achieve the best results on different benchmarks. Therefore, in
recent years, deep models have always been considered the first option for
emotion recognition. However, the debut of large language models (LLMs), such
as ChatGPT, has remarkably astonished the world due to their emerged
capabilities 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 in
terms of diverse aspects, including in-context learning, few-short learning,
accuracy, generalisation, and explanation. Moreover, we offer some insights and
pose other potential challenges, hoping to ignite broader discussions about
enhancing emotion recognition in the new era of advanced and generalised large
models.
