Scaling Diverse Table-Derived Pretraining Tasks for Robustness Against Distribution Shifts in Out-of-Domain Few-Shot Evaluation

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: Does scaling the number of diverse table-derived pretraining tasks improve robustness against distribution shifts in out-of-domain few-shot evaluation sets?

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