4.1.1 No use case. In most natural language understanding tasks, such as tasks in GLUE[106] and SuperGLUE[105],
fine-tuned models still have better performance, if such tasks come with rich well-annotated data and contain very few
out-of-distribution examples on test sets. For different tasks and datasets, the gap between small fine-tuned models and
LLMs varies.
Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond