AI coders are among us : Rethinking programming language grammar towards efficient code generation

• Main Authors: SUN Zhensu, DU Xiaoning, YANG Zhou, LI Li, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Code generation; Programming language; Large Language Model; Grammars and context-free language; Philosophical/theoretical foundations of artificial intelligence; AI-oriented grammar for Python; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/9886; https://ink.library.smu.edu.sg/context/sis_research/article/10886/viewcontent/3650212.3680347.pdf
• Institution: Singapore Management University

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.