Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs?

2025-03-07

AI Summary

This work investigates the persistent value of syntax-aware representations in billion-parameter large language models (LLMs) for code generation. While syntactic errors have markedly declined in ultra-large LLMs, the utility of explicit syntactic information remains questionable. To address this, we propose GrammarCoder—a family of models that explicitly integrate programming language grammar via context-free grammar (CFG)-guided decoding, syntax-constrained token prediction, and a Transformer-adapted syntax embedding enhancement module. Our study provides the first empirical evidence that syntactic information continues to significantly improve semantic discrimination—not merely syntactic correctness—in ultra-large LLMs, effectively mitigating semantic errors induced by minor code perturbations. On HumanEval+ and MBPP+, GrammarCoder achieves substantial accuracy gains; syntax error rates approach zero, and semantic error rates decrease by 12.7%.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Explores grammar-based code representation benefits for large language models.
• Assesses if grammar rules improve code generation accuracy in billion-scale models.
• Investigates grammar's role in reducing semantic errors in code generation.

Innovation

• Developed billion-scale GrammarCoder models
• Incorporated grammar rules in code generation
• Improved semantic differentiation in LLMs