106 citations · 307 across the 18 of their papers we have counts for
4 papers · 1 filter
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals
Ananth Balashankar, Xuezhi Wang, Yao Qin +5
Counterfactual Data Augmentation (CDA) is a commonly used technique for improving robustness in natural language classifiers. However, one fundamental challenge is how to discover…
Towards Robust Prompts on Vision-Language Models
Jindong Gu, Ahmad Beirami, Xuezhi Wang +3
With the advent of vision-language models (VLMs) that can perform in-context and prompt-based learning, how can we design prompting approaches that robustly generalize to distribut…
A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures
Amanda Baughan, Allison Mercurio, Ariel Liu +3
Despite huge gains in performance in natural language understanding via large language models in recent years, voice assistants still often fail to meet user expectations. In this…
What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel
Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan +2
A wide breadth of research has devised data augmentation approaches that can improve both accuracy and generalization performance for neural networks. However, augmented data can e…