Few-shot learning for sentence pair classification and its applications in software engineering

Few-shot learning-the ability to train models with access to limited data-has become increasingly popular in the NLP domain, as LLMs such as GPT and T0 have been empirically shown to achieve high performance in numerous tasks with access to just a handful of labeled examples. Smaller language models such as BERT and its variants have also been shown to achieve strong performance with just a handful of labeled examples when combined with few-shot learning algorithms like pattern-exploiting training (PET) and SetFit. The focus of this work is to investigate the performance of alternative few-shot learning approaches with BERT-based models. Specifically, vanilla fine-tuning, PET and SetFit are compared for numerous BERT-based checkpoints over an array of training set sizes. To facilitate this investigation, applications of few-shot learning are considered in software engineering. For each task, high-performance techniques and their associated model checkpoints are identified through detailed empirical analysis. Our results establish PET as a strong few-shot learning approach, and our analysis shows that with just a few hundred labeled examples it can achieve performance near that of fine-tuning on full-sized data sets.