@inproceedings{liang-etal-2025-grammar,
title = "Grammar-Based Code Representation: Is It a Worthy Pursuit for {LLM}s?",
author = "Liang, Qingyuan and
Zhang, Zhao and
Sun, Zeyu and
Lin, Zheng and
Luo, Qi and
Xiao, Yueyi and
Chen, Yizhou and
Zhang, Yuqun and
Zhang, Haotian and
Zhang, Lu and
Chen, Bin and
Xiong, Yingfei",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.807/",
doi = "10.18653/v1/2025.findings-acl.807",
pages = "15640--15653",
ISBN = "979-8-89176-256-5",
abstract = "Grammar serves as a cornerstone in programming languages and software engineering, providing frameworks to define the syntactic space and program structure. Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance. However, as language models scale to the billion level or beyond, syntax-level errors become rare, making it unclear whether grammar information still provides performance benefits. To explore this, we develop a series of billion-scale GrammarCoder models, incorporating grammar rules in the code generation process. Experiments on HumanEval (+) and MBPP (+) demonstrate a notable improvement in code generation accuracy. Further analysis shows that grammar-based representations enhance LLMs' ability to discern subtle code differences, reducing semantic errors caused by minor variations. These findings suggest that grammar-based code representations remain valuable even in billion-scale models, not only by maintaining syntax correctness but also by improving semantic differentiation."
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<abstract>Grammar serves as a cornerstone in programming languages and software engineering, providing frameworks to define the syntactic space and program structure. Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance. However, as language models scale to the billion level or beyond, syntax-level errors become rare, making it unclear whether grammar information still provides performance benefits. To explore this, we develop a series of billion-scale GrammarCoder models, incorporating grammar rules in the code generation process. Experiments on HumanEval (+) and MBPP (+) demonstrate a notable improvement in code generation accuracy. Further analysis shows that grammar-based representations enhance LLMs’ ability to discern subtle code differences, reducing semantic errors caused by minor variations. These findings suggest that grammar-based code representations remain valuable even in billion-scale models, not only by maintaining syntax correctness but also by improving semantic differentiation.</abstract>
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%0 Conference Proceedings
%T Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs?
%A Liang, Qingyuan
%A Zhang, Zhao
%A Sun, Zeyu
%A Lin, Zheng
%A Luo, Qi
%A Xiao, Yueyi
%A Chen, Yizhou
%A Zhang, Yuqun
%A Zhang, Haotian
%A Zhang, Lu
%A Chen, Bin
%A Xiong, Yingfei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F liang-etal-2025-grammar
%X Grammar serves as a cornerstone in programming languages and software engineering, providing frameworks to define the syntactic space and program structure. Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance. However, as language models scale to the billion level or beyond, syntax-level errors become rare, making it unclear whether grammar information still provides performance benefits. To explore this, we develop a series of billion-scale GrammarCoder models, incorporating grammar rules in the code generation process. Experiments on HumanEval (+) and MBPP (+) demonstrate a notable improvement in code generation accuracy. Further analysis shows that grammar-based representations enhance LLMs’ ability to discern subtle code differences, reducing semantic errors caused by minor variations. These findings suggest that grammar-based code representations remain valuable even in billion-scale models, not only by maintaining syntax correctness but also by improving semantic differentiation.
%R 10.18653/v1/2025.findings-acl.807
%U https://aclanthology.org/2025.findings-acl.807/
%U https://doi.org/10.18653/v1/2025.findings-acl.807
%P 15640-15653
Markdown (Informal)
[Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs?](https://aclanthology.org/2025.findings-acl.807/) (Liang et al., Findings 2025)
ACL
- Qingyuan Liang, Zhao Zhang, Zeyu Sun, Zheng Lin, Qi Luo, Yueyi Xiao, Yizhou Chen, Yuqun Zhang, Haotian Zhang, Lu Zhang, Bin Chen, and Yingfei Xiong. 2025. Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs?. In Findings of the Association for Computational Linguistics: ACL 2025, pages 15640–15653, Vienna, Austria. Association for Computational Linguistics.