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.
@article{arxiv.2507.09135,
title = {Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps},
author = {Yalong Du and Chaozheng Wang and Huaijin Wang},
journal= {arXiv preprint arXiv:2507.09135},
year = {2025}
}