  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.
