High Quality Distractors

High-quality distractors are crucial for robustly evaluating and improving machine learning models, particularly in vision and language tasks. Current research focuses on generating effective distractors for various applications, including multiple-choice questions, visual object recognition, and reinforcement learning, often employing large language models (LLMs) and advanced neural network architectures like transformers. This work aims to create more challenging and realistic benchmarks by developing methods to generate diverse, plausible, and semantically meaningful distractors that expose model weaknesses and drive improvements in model robustness and generalization capabilities. The resulting advancements have implications for improving the reliability and performance of AI systems across numerous domains.