From heuristic filters to AI classifiers: practical techniques for curating trillion-token datasets
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Training a language model on the raw internet is like trying to learn from every conversation happening in the world simultaneously. Most of it is noise. Some of it is toxic. Much of it repeats endlessly. The quality of what goes in directly determines the quality of what comes out.
Data quality isn’t just about removing obviously bad content. It’s about understanding what makes text valuable for learning, then systematically identifying and preserving that value at a massive scale.
Understanding Quality Signals
Before you can filter data, you need to define what “quality” means. For language model tr…
From heuristic filters to AI classifiers: practical techniques for curating trillion-token datasets
14 min readJust now
–
Press enter or click to view image in full size
Training a language model on the raw internet is like trying to learn from every conversation happening in the world simultaneously. Most of it is noise. Some of it is toxic. Much of it repeats endlessly. The quality of what goes in directly determines the quality of what comes out.
Data quality isn’t just about removing obviously bad content. It’s about understanding what makes text valuable for learning, then systematically identifying and preserving that value at a massive scale.
Understanding Quality Signals
Before you can filter data, you need to define what “quality” means. For language model training, quality is multi-dimensional. A document might have excellent grammar but no useful information. Another might be packed with knowledge but formatted terribly.
Content quality comes down to whether the text actually contains useful information. Is it coherent? Does it teach something or demonstrate reasoning? A well-written tutorial about debugging Python code has high content quality. A spam page stuffed with keywords has none.