Noisy Web

The "noisy web" refers to the challenge of extracting usable data from the vast, uncurated content of the internet, which is rife with errors, inconsistencies, and irrelevant information. Current research focuses on developing robust methods for data selection and cleaning, employing techniques like contrastive learning, variance alignment scores, and perplexity-based filtering to identify and remove low-quality or harmful content. These efforts are crucial for training large language models and other machine learning systems, as the quality and scale of training data directly impact model performance and reliability, ultimately affecting various applications from image recognition to natural language processing.