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.

翻译：情感识别或情感计算自诞生以来，因其广泛的应用而逐渐成为一个活跃的研究课题。在过去几十年中，情感识别模型已从统计浅层模型逐步过渡到基于神经网络的深度模型，这显著提升了情感识别模型的性能，并在不同基准测试中持续取得最佳结果。因此，近年来深度模型一直被视为情感识别的首选方案。然而，大型语言模型（LLMs）的出现，如ChatGPT，凭借其零样本/少样本学习、上下文学习、思维链等以往深度模型从未展现的能力，令世界为之震惊。本文从多个维度全面探究了LLMs在情感识别中的表现，包括上下文学习、少样本学习、准确性、泛化能力及可解释性。此外，我们提出了一些见解并指出了其他潜在挑战，期望能引发关于在高级通用大模型新时代提升情感识别能力的更广泛讨论。