Large language models (LLMs) have made significant advancements in code-related tasks, yet many LLMs treat code as simple sequences, neglecting its structured nature. We introduce AST-T5, a novel pretraining paradigm that leverages the Abstract Syntax Tree (AST) for enhanced code generation, transpilation, and understanding. Using dynamic programming, our AST-Aware Segmentation retains code structure, while our AST-Aware Span Corruption objective equips the model to reconstruct various code structures. Unlike other models, AST-T5 avoids intricate program analyses or architectural changes, so it integrates seamlessly with any encoder-decoder Transformer. Evaluations show that AST-T5 consistently outperforms similar-sized LMs across various code-related tasks. Structure-awareness makes AST-T5 particularly powerful in code-to-code tasks, surpassing CodeT5 by 2 points in exact match score for the Bugs2Fix task and by 3 points in exact match score for Java-C# Transpilation in CodeXGLUE. Our code and model are publicly available at https://github.com/gonglinyuan/ast_t5.
AST-T5: Structure-Aware Pretraining for Code Generation and Understanding
AST-T5, a pretraining paradigm leveraging Abstract Syntax Trees, enhances code generation, transpilation, and understanding, particularly in code-to-code tasks, outperforming similar models like CodeT5.
- Year
- 2024
- Venue
- arXiv 2024
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- 3
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2401.03003v4ARXIV-DEFAULT
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