Improving and Simplifying Pattern Exploiting Training
Derek Tam, Rakesh R. Menon, Mohit Bansal, Shashank Srivastava, Colin Raffel · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
Recently, pre-trained language models (LMs) have achieved strong performance when finetuned on difficult benchmarks like Super-GLUE.However, performance can suffer when there are very few labeled examples available for fine-tuning.Pattern Exploiting Training (PET) is a recent approach that leverages patterns for few-shot learning.However, PET uses task-specific unlabeled data.In this paper, we focus on few shot learning without any unlabeled data and introduce ADAPET, which modifies PET's objective to provide denser supervision during fine-tuning.As a result, ADAPET outperforms PET on Su-perGLUE without any task-specific unlabeled data.