Web Scale

Web-scale data analysis focuses on leveraging massive internet datasets to train and improve machine learning models, particularly large language models (LLMs). Current research emphasizes efficient data handling techniques, including dataset pruning and semantic deduplication, to reduce computational costs while maintaining or improving model performance. This work is crucial for advancing various applications, such as improved search ranking, more accurate speech recognition and translation, and the development of specialized LLMs for domains like finance, all while addressing biases and ensuring data integrity in the face of potential poisoning attacks.