Recent advances have led to the availability of many pre-trained language models (PLMs); however, a question that remains is how much data is truly needed to fine-tune PLMs for downstream tasks? In this work, we introduce DEFT, a data-efficient fine-tuning framework that leverages unsupervised core-set selection to minimize the amount of data needed to fine-tune PLMs for downstream tasks. We demonstrate the efficacy of our DEFT framework in the context of text-editing LMs, and compare to the state-of-the art text-editing model, CoEDIT. Our quantitative and qualitative results demonstrate that DEFT models are just as accurate as CoEDIT while being finetuned on ~70% less data.

翻译：摘要：近期进展使许多预训练语言模型（PLMs）得以广泛应用，但一个尚未解决的问题是：针对下游任务微调PLMs究竟需要多少数据？本文提出DEFT，一种数据高效微调框架，通过无监督核心集选择来最小化微调PLMs所需的数据量。我们在文本编辑语言模型场景中验证了DEFT框架的有效性，并与当前最先进的文本编辑模型CoEDIT进行对比。定量与定性结果表明，DEFT模型在微调数据量减少约70%的情况下，仍能达到与CoEDIT相当的准确率。