Can ChatGPT's Responses Boost Traditional Natural Language Processing?
Mostafa M. Amin Erik Cambria Björn W. Schuller
The employment of foundation models is steadily expanding, especially with
the launch of ChatGPT and the release of other foundation models. These models
have shown the potential of emerging capabilities to solve problems, without
being particularly trained to solve. A previous work demonstrated these
emerging capabilities in affective computing tasks; the performance quality was
similar to traditional Natural Language Processing (NLP) techniques, but
falling short of specialised trained models, like fine-tuning of the RoBERTa
language model. In this work, we extend this by exploring if ChatGPT has novel
knowledge that would enhance existing specialised models when they are fused
together. We achieve this by investigating the utility of verbose responses
from ChatGPT about solving a downstream task, in addition to studying the
utility of fusing that with existing NLP methods. The study is conducted on
three affective computing problems, namely sentiment analysis, suicide tendency
detection, and big-five personality assessment. The results conclude that
ChatGPT has indeed novel knowledge that can improve existing NLP techniques by
way of fusion, be it early or late fusion.