Pre-trained Language Models (PLMs) can be accurately fine-tuned for downstream text processing tasks. Recently, researchers have introduced several parameter-efficient fine-tuning methods that optimize input prompts or adjust a small number of model parameters (e.g LoRA). In this study, we explore the impact of altering the input text of the original task in conjunction with parameter-efficient fine-tuning methods. To most effectively rewrite the input text, we train a few-shot paraphrase model with a Maximum-Marginal Likelihood objective. Using six few-shot text classification datasets, we show that enriching data with paraphrases at train and test time enhances the performance beyond what can be achieved with parameter-efficient fine-tuning alone. The code used for our experiments can be found at https://github.com/SaeedNajafi/RIFF.

翻译：预训练语言模型（PLMs）能够通过微调准确适应下游文本处理任务。近年来，研究者提出了多种参数高效的微调方法，例如优化输入提示或调整少量模型参数（如LoRA）。本研究探讨了在结合参数高效微调方法的同时，改变原始任务输入文本所产生的影响。为最有效地重写输入文本，我们采用最大边际似然目标训练了一个少样本复述模型。通过在六个少样本文本分类数据集上的实验，我们证明在训练和测试阶段使用复述数据增强能够进一步提升性能，其效果超越了单独使用参数高效微调方法所能达到的水平。实验所用代码已公开于 https://github.com/SaeedNajafi/RIFF。