On Measuring the Intrinsic Few-Shot Hardness of Datasets

• URL: http://arxiv.org/abs/2211.09113v1
• Date: Wed, 16 Nov 2022 18:53:52 GMT
• Title: On Measuring the Intrinsic Few-Shot Hardness of Datasets
• Authors: Xinran Zhao, Shikhar Murty, Christopher D. Manning
• Abstract summary: We show that few-shot hardness may be intrinsic to datasets, for a given pre-trained model. We propose a simple and lightweight metric called "Spread" that captures the intuition that few-shot learning is made possible. Our metric better accounts for few-shot hardness compared to existing notions of hardness, and is 8-100x faster to compute.
• Score: 49.37562545777455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While advances in pre-training have led to dramatic improvements in few-shot learning of NLP tasks, there is limited understanding of what drives successful few-shot adaptation in datasets. In particular, given a new dataset and a pre-trained model, what properties of the dataset make it \emph{few-shot learnable} and are these properties independent of the specific adaptation techniques used? We consider an extensive set of recent few-shot learning methods, and show that their performance across a large number of datasets is highly correlated, showing that few-shot hardness may be intrinsic to datasets, for a given pre-trained model. To estimate intrinsic few-shot hardness, we then propose a simple and lightweight metric called "Spread" that captures the intuition that few-shot learning is made possible by exploiting feature-space invariances between training and test samples. Our metric better accounts for few-shot hardness compared to existing notions of hardness, and is ~8-100x faster to compute.

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