Comparative Performance of Few-Shot Prompting and Full Fine-Tuning on RobustBench under Adversarial Perturbations

State-of-the-art few-shot learning (FSL) methods leverage prompt-based fine-tuning to obtain remarkable results for natural language understanding (NLU) tasks. While much of the prior FSL methods focus on improving downstream task performance, there is a limited understanding of the adversarial robustness of such methods. In this work, we conduct an extensive study of several state-of-the-art FSL methods to assess their robustness to adversarial perturbations. To better understand the impact of various factors towards robustness (or the lack of it), we evaluate prompt-based FSL methods against

Research goal: What is the comparative performance of prompt-based few-shot learning versus full fine-tuning on RobustBench under adversarial perturbations for natural language understanding tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.