Irrelevant Information

Irrelevant information significantly impacts the performance of various machine learning models, particularly large language models (LLMs) and image classifiers. Current research focuses on developing methods to mitigate the negative effects of this irrelevant data, including techniques like filtering irrelevant information, generating alterfactual explanations to highlight its impact, and training models to be more robust to distractions. These efforts are crucial for improving the reliability and accuracy of AI systems across diverse applications, from question answering and summarization to autonomous driving and robotic control.