Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
9-2024
Abstract
Artificial Intelligence (AI) models have emerged as another important audience for programming languages alongside humans and machines, as we enter the era of large language models (LLMs). LLMs can now perform well in coding competitions and even write programs like developers to solve various tasks, including mathematical problems. However, the grammar and layout of current programs are designed to cater the needs of human developers -- with many grammar tokens and formatting tokens being used to make the code easier for humans to read. While this is helpful, such a design adds unnecessary computational work for LLMs, as each token they either use or produce consumes computational resources. To improve inference efficiency and reduce computational costs, we propose the concept of AI-oriented grammar.This aims to represent code in a way that better suits the working mechanism of AI models. Code written with AI-oriented grammar discards formats and uses a minimum number of tokens to convey code semantics effectively. To demonstrate the feasibility of this concept, we explore and implement the first AI-oriented grammar for Python, named Simple Python (SimPy). SimPy is crafted by revising the original Python grammar through a series of heuristic rules. Programs written in SimPy maintain identical Abstract Syntax Tree (AST) structures to those in standard Python. This allows for not only execution via a modified AST parser, but also seamless transformation between programs written in Python and SimPy, enabling human developers and LLMs to use Python and SimPy, respectively, when they need to collaborate. We also look into methods to help existing LLMs understand and use SimPy effectively. In the experiments, compared with Python, SimPy enables a reduction in token usage by 13.5% and 10.4% for CodeLlama and GPT-4, respectively, when completing the same set of code-related tasks. Additionally, these models can maintain or even improve their performance when using SimPy instead of Python for these tasks. With these promising results, we call for further contributions to the development of AI-oriented program grammar within our community.
Keywords
Code generation, Programming language, Large Language Model, Grammars and context-free language, Philosophical/theoretical foundations of artificial intelligence, AI-oriented grammar for Python
Discipline
Software Engineering
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2024) : Vienna, Austria, September 16-20
First Page
1124
Last Page
1136
Identifier
10.1145/3650212.3680347
Publisher
Association for Computing Machinery
City or Country
New York, NY, USA
Citation
SUN Zhensu; DU Xiaoning; YANG Zhou; LI Li; and LO, David.
AI coders are among us : Rethinking programming language grammar towards efficient code generation. (2024). Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2024) : Vienna, Austria, September 16-20. 1124-1136.
Available at: https://ink.library.smu.edu.sg/sis_research/9886
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3650212.3680347
