Better Language Models of Code through Self-Improvement

• URL: http://arxiv.org/abs/2304.01228v2
• Date: Wed, 10 May 2023 02:36:40 GMT
• Title: Better Language Models of Code through Self-Improvement
• Authors: Hung Quoc To, Nghi D. Q. Bui, Jin Guo, Tien N. Nguyen
• Abstract summary: We propose a simple data augmentation framework for pre-trained language models for code (PLMCs) Our framework utilizes knowledge gained during the pre-training and fine-tuning stage to generate pseudo data, which is then used as training data for the next step. The results show that our framework significantly improves PLMCs' performance in code-related sequence generation tasks.
• Score: 18.75015225501755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-trained language models for code (PLMCs) have gained attention in recent research. These models are pre-trained on large-scale datasets using multi-modal objectives. However, fine-tuning them requires extensive supervision and is limited by the size of the dataset provided. We aim to improve this issue by proposing a simple data augmentation framework. Our framework utilizes knowledge gained during the pre-training and fine-tuning stage to generate pseudo data, which is then used as training data for the next step. We incorporate this framework into the state-of-the-art language models, such as CodeT5, CodeBERT, and UnixCoder. The results show that our framework significantly improves PLMCs' performance in code-related sequence generation tasks, such as code summarization and code generation in the CodeXGLUE benchmark.

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