Scaling Self-Supervised Pretraining Data for Few-Shot Meta-Learners on HELM and BIG-Bench Benchmarks

Abstract

Abstract: BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models. Language models have already made good progress on this benchmark, with the best model in the BIG-Bench paper outperforming average reported human-rater results on 65\% of the BIG-Bench tasks via few-shot prompting. But on what tasks do language models fall short of average human-rater performance, and are those tasks actually unsolvable by current language models? In this work, we focus on a suite of 23 challenging BIG-Bench tasks which we

Research Question

How does the scaling of self-supervised pretraining data size affect the performance of few-shot meta-learners on language model benchmarks like HELM or BIG-Bench?

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