3 accepted papers
Recently, large language models (LLMs) have significantly improved the performance of text-to-SQL systems. Nevertheless, many state-of-the-art (SOTA) approaches have overlooked the critical aspect of system robustness. Our experiments reveal that while LLM-driven methods excel on standard datasets,…
Deep neural networks are vulnerable to adversarial attacks leading to erroneous outputs. Adversarial training has been recognized as one of the most effective methods to counter such attacks. However existing adversarial training techniques have predominantly been evaluated on balanced datasets wher…
Adversarial Robustness Distillation (ARD) aims to transfer the robustness of large teacher models to small student models, facilitating the attainment of robust performance on resource-limited devices. However, existing research on ARD primarily focuses on the overall robustness of student models, o…