Abstract
Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, a gap remains between their output and the problem-solving strategies of human developers. Unlike humans, who spend substantial time disambiguating requirements through iterative dialogue, LLMs often generate code despite ambiguities in natural language requirements, leading to unreliable solu