Context Perturbation

Context perturbation involves subtly altering input data to probe the robustness and explainability of machine learning models. Current research focuses on applying this technique to improve the trustworthiness of AI systems across diverse applications, including explainable AI (XAI), question answering, news sentiment analysis, and grammatical error correction, often employing techniques like masked language models and diffusion models. These studies highlight the need for models that are not only accurate but also reliable and interpretable, leading to advancements in model design and evaluation methodologies with implications for various fields relying on AI.