2 research outputs found
Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models
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
Customising General Large Language Models for Specialised Emotion Recognition Tasks
The advent of large language models (LLMs) has gained tremendous attention
over the past year. Previous studies have shown the astonishing performance of
LLMs not only in other tasks but also in emotion recognition in terms of
accuracy, universality, explanation, robustness, few/zero-shot learning, and
others. Leveraging the capability of LLMs inevitably becomes an essential
solution for emotion recognition. To this end, we further comprehensively
investigate how LLMs perform in linguistic emotion recognition if we
concentrate on this specific task. Specifically, we exemplify a publicly
available and widely used LLM -- Chat General Language Model, and customise it
for our target by using two different modal adaptation techniques, i.e., deep
prompt tuning and low-rank adaptation. The experimental results obtained on six
widely used datasets present that the adapted LLM can easily outperform other
state-of-the-art but specialised deep models. This indicates the strong
transferability and feasibility of LLMs in the field of emotion recognition