Underspecification Does Not Imply Incoherence in LLM Code Generation (arxiv.org)

Recent research challenges the prevailing assumption that underspecified task descriptions in Large Language Models (LLMs) lead to incoherent outputs in code generation. The study reveals that rather than producing a variety of semantically distinct interpretations when faced with ambiguous instructions, LLMs often converge on a single, coherent but incorrect interpretation—what the authors term "detrimental semantic collapse." This phenomenon affects a significant portion of tasks across various benchmarks, illustrating that over 10% of instances in the MBPP benchmark and even higher in others display this failure mode. This finding is critical for the AI and machine learning community as it highlights a blind spot in our understanding of how LLMs handle ambiguity. Traditional measures of disambiguation and correctness may not adequately capture this issue, as they often assume that incoherence indicates an inability to understand prompt underspecification. By exposing this flaw, the paper calls for a reevaluation of how task interpretations are assessed, emphasizing the need for enhanced techniques that can better identify and mitigate the risks associated with semantic collapse in code generation.