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        <title>Better Language Models of Code through Self-Improvement</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Better Language Models of Code through Self-Improvement
%A To, Hung
%A Bui, Nghi
%A Guo, Jin L.C.
%A Nguyen, Tien
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F to-etal-2023-better
%X 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.
%R 10.18653/v1/2023.findings-acl.823
%U https://aclanthology.org/2023.findings-acl.823
%U https://doi.org/10.18653/v1/2023.findings-acl.823
%P 12994-13002

Markdown (Informal)

[Better Language Models of Code through Self-Improvement](https://aclanthology.org/2023.findings-acl.823) (To et al., Findings 2023)

• Better Language Models of Code through Self-Improvement (To et al., Findings 2023)

ACL

• Hung To, Nghi Bui, Jin L.C. Guo, and Tien Nguyen. 2023. Better Language Models of Code through Self-Improvement. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12994–13002, Toronto, Canada. Association for Computational Linguistics.