
polish the paper NAME_1.

Almost all models reach the same score after being sufficiently trained in the same setting, as shown in \ref{fig:Fine-tune}. The scores indicate the intrinsic difficulty of the tasks. As the difficulty of the task changes, the gap between fine-tuning and zero-shot also shifts. Given a limited number of samples in the training dataset, \ref{fig:few-shot} shows the performance of the models in few-shot scenarios. The model "GUR-FULL" is the best one across all tasks.

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