RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models

• URL: http://arxiv.org/abs/2403.02271v2
• Date: Thu, 6 Jun 2024 14:43:30 GMT
• Title: RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models
• Authors: Saeed Najafi, Alona Fyshe,
• Abstract summary: 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. 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.
• Score: 4.085425430499285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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