When building large language models, the scale and quality of pretraining data directly impact model performance. Should we prioritize massive amounts of text, or focus more on data diversity and noise control? Additionally, how can we balance data distribution across different domains to avoid model bias toward specific scenarios? What experiences or methods have you found useful in real-world projects?
How do we choose the right training data scale, quality, and diversity during the pretraining phase of large language models to enhance their generalization ability?
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Hey everyone,
While preparing a large language model for a smart home project, I kept running into the same question about data volume versus diversity. We found that just dumping a massive pile of raw text without filtering tends to introduce “noise” that drags down the model’s overall performance. So we started with a clean, high-quality seed dataset, then gradually layered in different domains (education, tech, healthcare, etc.) using a “curriculum learning” approach so the model builds a balanced sense of patterns.
I’d also suggest adding automatic filters to strip out duplicates and noisy entries, plus a dynamic weighting system that gives priority to high-quality text and a fair spread across domains during training. In our last project we followed up the general training phase with “domain-adaptive pre-training,” which cut the bias toward any single scenario and boosted performance on a wide range of tasks. Same here—after rolling out these steps we saw a clear uptick in generalization and fewer mistakes in the less-represented domains. Hope this experience helps you out too.