Web Crawled Data

Web-crawled data, encompassing vast amounts of text and multimedia from the internet, is increasingly used to train and fine-tune large language models (LLMs) and other machine learning systems. Current research focuses on improving data quality by filtering noise, aligning web data with higher-quality sources, and leveraging techniques like contrastive learning and LLM-generated parallel data to enhance model performance. This work is significant because it addresses the challenges of data scarcity and cost in training powerful models, enabling advancements in various applications, including natural language processing, machine translation, and multimodal learning.