On Measuring the Intrinsic Few-Shot Hardness of Datasets
This addresses the need for efficient and generalizable metrics to predict few-shot learning performance across datasets, though it is incremental as it builds on existing notions of hardness.
The paper tackled the problem of understanding what makes datasets few-shot learnable, showing that few-shot hardness is intrinsic to datasets for a given pre-trained model, and proposed a metric called 'Spread' that better accounts for this hardness and is 8-100x faster to compute.
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.