Task Abstention for Large Language Models in Code Generation
Large language models (LLMs) have revolutionized automated code generation. One serious concern, however, is the so-called ``hallucination'', i.e., LLMs may generate seemingly plausible but functionally incorrect code. In this paper, we study the task abstention problem, i.e., determining whether a given LLM should abstain from performing a specific code generation task to avoid likely hallucination. Our approach features a calibrated abstention rule, grounded in the principles of multiple hypothesis testing. The rule assesses generation consistency through code execution outcomes, allowing it to handle syntactic diversity of semantically equivalent code without reliance on oracle test ca...
| Authors | Yanke Zhou, Yuhao Tan, Senrong Xu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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