The dataset used to train a machine learning model. For LLMs, this typically includes web pages, books, code, and other text corpora totaling billions of tok...
All the books, websites, and conversations the AI read to learn how to talk — like going to a really, really big school.
The huge collection of text, images, or other data that an AI studied to learn. The better and bigger the training data, the smarter the AI.
The dataset used to train a machine learning model. For LLMs, this typically includes web pages, books, code, and other text corpora totaling billions of tokens.
The corpus of labeled or unlabeled examples used during the optimization of model parameters. Data quality, diversity, and scale directly impact model capabilities and biases.
The empirical distribution D from which training examples are drawn, governing the model's inductive bias and generalization bounds — subject to distribution shift, label noise, memorization vs. compression tradeoffs, and data contamination risks.
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