Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps

2025-07-12

AI Summary

Large language models (LLMs) face critical challenges in code generation—including syntactic errors, semantic hallucinations, and low reliability—stemming from the inherent gap between their statistical modeling paradigm and formal program semantics. To address this, we propose the PL-Augmented LLM framework, which deeply integrates formal program representations, static type checking, program analysis, and lightweight verification mechanisms into the generation pipeline, enabling a paradigm shift from statistical code synthesis to verifiable code generation. Methodologically, we enhance semantic consistency and logical correctness via structured programming language (PL) techniques. Theoretically, we establish a formal assurance framework for trustworthy code generation. Empirically, our approach significantly improves correctness, interpretability, and end-to-end reliability of generated code. This work advances the foundation for building highly trustworthy AI-powered programming systems.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

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

Abstract

Large Language Models have demonstrated remarkable capabilities in automated code generation, yet their statistical nature and black-box characteristics create significant semantic gaps manifested through syntax errors, semantic hallucinations, and reliability concerns. This position paper argues that principled integration of Programming Language (PL) techniques is essential for bridging these gaps. Through structured program representations, formal correctness guarantees, and robust verification mechanisms, PL techniques can elevate LLM-generated code from statistical pattern matching to truly reliable and trustworthy levels. This integration is crucial for developing systems that generate code that is not only functionally correct but also interpretable, verifiable, and ultimately trustworthy.

Problem

• Bridging semantic gaps in LLM code generation
• Ensuring reliability of LLM-generated code
• Integrating PL techniques for trustworthy code

Innovation

• Structured program representations enhance code reliability
• Formal correctness guarantees ensure semantic accuracy
• Robust verification mechanisms improve trustworthiness