Functional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification 事件

Functional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification arXiv:2605.28500v1 Announce Type: new Abstract: Large language models have shown impressive capabilities in code generation, yet they often produce functionally incorrect code. Uncertainty quantification (UQ) methods have emerged as a promising approach for detecting hallucinations in natural language generation, but their effectiveness for code generation tasks remains underexplored. We