Before revision:  To address this problem, we publish our few-shot sets. 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.
After revision:  To address this problem, we publish our few-shot transfer, which highlights the role of lexical features. Additionally, we show that large models heavily rely on lexical hints when finetuned on a few shots and then overfit quickly.