Abstract:Nowadays, owing to the superior capacity of the large pre-trained language models (PLM), the PLM-based zero-shot learning has shown promising performances on various natural language processing tasks. There are emerging interests in further exploring the zero-shot learning potential of PLMs. Among them, Ze-roGen (Ye et al., 2022a) attempts to purely use PLM to generate data and train a tiny model without relying on any task-specific annotation. Despite its remarkable results, we observe that the synthesized da… Show more
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