Can ChatGPT's Responses Boost Traditional Natural Language Processing?
This work addresses the problem of boosting specialized NLP models for affective computing, but it is incremental as it builds on prior research by exploring fusion techniques.
The study investigated whether ChatGPT's responses could enhance traditional NLP models through fusion, finding that ChatGPT provides novel knowledge that improves performance on affective computing tasks like sentiment analysis, suicide detection, and personality assessment.
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.