Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information. (arXiv:2110.08420v2 [cs.CL] UPDATED)

Web: http://arxiv.org/abs/2110.08420

Estimating the difficulty of a dataset typically involves comparing
state-of-the-art models to humans; the bigger the performance gap, the harder
the dataset is said to be. However, this comparison provides little
understanding of how difficult each instance in a given distribution is, or
what attributes make the dataset difficult for a given model. To address these
questions, we frame dataset difficulty -- w.r.t. a model $\mathcal{V}$ -- as
the lack of $\mathcal{V}$-$\textit{usable information}$ (Xu et al., 2019),
where a lower …

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