A research team from New York University, Facebook AI, and a CIFAR Fellow in Learning in Machines & Brains raise doubts regarding large-scale pretrained language models’ few-shot learning abilities. The researchers re-evaluate such abilities with held-out examples unavailable, which they propose constitutes “true few-shot learning.”

Here is a quick read: NYU, Facebook & CIFAR Present ‘True Few-Shot Learning’ for Language Models Whose Few-Shot Ability They Say Is Overestimated.

The paper True Few-Shot Learning with Language Models is on arXiv.

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