Stability Analysis of ChatGPT-based Sentiment Analysis in AI Quality
  Assurance
Tinghui Ouyang AprilPyone MaungMaung Koichi Konishi Yoshiki Seo Isao Echizen
In the era of large AI models, the complex architecture and vast parameters
present substantial challenges for effective AI quality management (AIQM), e.g.
large language model (LLM). This paper focuses on investigating the quality
assurance of a specific LLM-based AI product--a ChatGPT-based sentiment
analysis system. The study delves into stability issues related to both the
operation and robustness of the expansive AI model on which ChatGPT is based.
Experimental analysis is conducted using benchmark datasets for sentiment
analysis. The results reveal that the constructed ChatGPT-based sentiment
analysis system exhibits uncertainty, which is attributed to various
operational factors. It demonstrated that the system also exhibits stability
issues in handling conventional small text attacks involving robustness.