标题： 立场论文：用于弥合LLM代码生成语义差距的编程语言技术 显示英文标题

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

摘要： 大型语言模型在自动代码生成方面表现出令人瞩目的能力，但其统计性质和黑盒特性导致了显著的语义差距，表现为语法错误、语义幻觉和可靠性问题。 本文认为，将编程语言（PL）技术进行有原则的整合对于弥合这些差距至关重要。 通过结构化的程序表示、形式化正确性保证和强大的验证机制，PL技术可以将LLM生成的代码从统计模式匹配提升到真正可靠和值得信赖的水平。 这种整合对于开发不仅功能正确，而且可解释、可验证并最终值得信赖的代码生成系统至关重要。 显示英文摘要

摘要： 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.