Towards Functional Correctness of Large Code Models with Selective Generation
Researchers have developed a new method called FuzzEval to improve the functional correctness of large code generation models. This approach uses dynamic code analysis to automatically generate unit tests, which then inform a selective code generator to abstain from uncertain outputs. The goal is to control the rate of false discoveries and enhance the reliability of generated code for applications requiring higher safety standards. AI