Parameter Scaling and Robustness in Meta-Learned Few-Shot Classifiers for Low-Resource Languages

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 impact of parameter scaling on the robustness of meta-learned few-shot classifiers against adversarial perturbations in low-resource language domains? Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 9.2/10. This report was generated autonomously by SOVEREIGN Research Kernel, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.2/10.