CodePrompt: Task-Agnostic Prefix Tuning for Program and Language Generation
YunSeok Choi, Jee-Hyong Lee
Abstract
In order to solve the inefficient parameter update and storage issues of fine-tuning in Natural Language Generation (NLG) tasks, prompt-tuning methods have emerged as lightweight alternatives.Furthermore, efforts to reduce the gap between pre-training and fine-tuning have shown successful results in low-resource settings.As large Pre-trained Language Models (PLMs) for Program and Language Generation (PLG) tasks are constantly being developed, prompt tuning methods are necessary for the tasks.However, due to the gap between pre-training and fine-tuning different from PLMs for natural language, a prompt tuning method that reflects the traits of PLM for program language is needed.In this paper, we propose a Task-Agnostic prompt tuning method for the PLG tasks, CodePrompt, that combines Input-Dependent Prompt Template (to bridge the gap between pre-training and fine-tuning of PLMs for program and language) and Corpus-Specific Prefix Tuning (to update the parameters of PLMs for program and language efficiently).Also, we propose a method to provide richer prefix word information for limited prefix lengths. We prove that our method is effective in three PLG tasks, not only in the full-data setting but also in the low-resource setting and cross-domain setting.