  The GPT-3.5 models have demonstrated impressive performance in various
Natural Language Processing (NLP) tasks, showcasing their strong understanding
and reasoning capabilities. However, their robustness and abilities to handle
various complexities of the open world have yet to be explored, which is
especially crucial in assessing the stability of models and is a key aspect of
trustworthy AI. In this study, we perform a comprehensive experimental analysis
of GPT-3.5, exploring its robustness using 21 datasets (about 116K test
samples) with 66 text transformations from TextFlint that cover 9 popular
Natural Language Understanding (NLU) tasks. Our findings indicate that while
GPT-3.5 outperforms existing fine-tuned models on some tasks, it still
encounters significant robustness degradation, such as its average performance
dropping by up to 35.74\% and 43.59\% in natural language inference and
sentiment analysis tasks, respectively. We also show that GPT-3.5 faces some
specific robustness challenges, including robustness instability, prompt
sensitivity, and number sensitivity. These insights are valuable for
understanding its limitations and guiding future research in addressing these
challenges to enhance GPT-3.5's overall performance and generalization
abilities.
