Sourcing and Collecting Data for Training Large Language Models

Author(s): M

Originally published on Towards AI.

Real-world insights from FineWeb, DCLM, The Stack v2, and modern LLM training

When people talk about training language models, the conversation often jumps straight to architecture choices or training techniques. But here’s the reality: you can have the most elegant transformer architecture and perfectly tuned hyperparameters, but if your training data is messy, biased, or poorly sourced, you’re building on quicksand.

The article discusses the importance of sourcing high-quality training data for language models, emphasizing that although there is an abundance of text data available online, the true challenge lies in identifying and using the right data responsibly. It highlights various sources of training data, such as the Common Crawl, curated datasets like FineWeb, and the methodologies for extracting, filtering, and processing data. It also explores the significance of copyright and legal considerations, ending with practical lessons learned from recent model training efforts, showcasing how effective data collection and quality control can lead to better-performing language models.

Read the full blog for free on Medium.

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Published via Towards AI