Hello, the quality and size of the datasets used in training LLMs significantly impact model performance. So, is the importance solely in volume, or is diversity also critical? Additionally, what should we pay attention to regarding data cleaning and bias mitigation? I'd like to dive a bit deeper into the theory—anyone with experience to share would be greatly appreciated.
How do LLMs affect data?
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I think the most critical issue is diversity, more than volume. Models fed with uniform data start going off the rails, and bias goes on the offensive, no joke. Even a tiny bit of noise in the cleaning process throws the model into chaos, so precision is non-negotiable.