Adversarial Fine-Tuning of CodeT5 for Zero-Shot Robustness and Semantic Consistency

Machine learning (ML) systems have introduced significant advances in various fields, due to the introduction of highly complex models. Despite their success, it has been shown multiple times that machine learning models are prone to imperceptible perturbations that can severely degrade their accuracy. So far, existing studies have primarily focused on models where supervision across all classes were available. In constrast, Zero-shot Learning (ZSL) and Generalized Zero-shot Learning (GZSL) tasks inherently lack supervision across all classes. In this paper, we present a study aimed on evaluat

Research goal: What is the impact of fine-tuning CodeT5 with adversarial training on its semantic consistency and robustness accuracy in generalized zero-shot learning tasks compared to standard fine-tuning?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.8/10.