Before revision:  In a study of why few-shot learning outperforms zero-shot transfer, we show that large models heavily rely on lexical hints when finetuned on a few shots and then overfit quickly. We evaluate different methods that use few-shot annotations, but do not observe significant improvements over the baseline.
After revision:  In a study of why few-shot learning outperforms zero-shot transfer, we show that a straightforward full model finetuning approach is quite effective for few-shot annotations, but do not observe significant improvements over the baseline.