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    <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.</abstract>
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%0 Conference Proceedings
%T RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models
%A Najafi, Saeed
%A Fyshe, Alona
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F najafi-fyshe-2024-riff
%X 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.
%U https://aclanthology.org/2024.findings-acl.85
%P 1447-1466

Markdown (Informal)

[RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models](https://aclanthology.org/2024.findings-acl.85) (Najafi & Fyshe, Findings 2024)

• RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models (Najafi & Fyshe, Findings 2024)

ACL

• Saeed Najafi and Alona Fyshe. 2024. RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 1447–1466, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.