@inproceedings{to-etal-2023-better,
    title = "Better Language Models of Code through Self-Improvement",
    author = "To, Hung  and
      Bui, Nghi  and
      Guo, Jin L.C.  and
      Nguyen, Tien",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.823",
    doi = "10.18653/v1/2023.findings-acl.823",
    pages = "12994--13002",
    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 data augmentation framework using knowledge distillation. Our framework utilizes knowledge gained during the pre-training and fine-tuning stage to augment training data, which is then used 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 sequence-generation tasks, such as code summarization and code generation in the CodeXGLUE benchmark.",
}

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 data augmentation framework using knowledge distillation. Our framework utilizes knowledge gained during the pre-training and fine-tuning stage to augment training data, which is then used 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 sequence-generation tasks, such as code summarization and code generation in the CodeXGLUE benchmark.
To, Hung Bui, Nghi Guo, Jin L.C. Nguyen, Tien
Better Language Models of Code through Self-Improvement