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Probability distribution

As explained in LLMs in Production

A data distribution is the pattern of frequencies or probabilities with which data values or feature combinations occur.

Before training an LLM, ask whether the data represents the task you want it to perform. A feature’s distribution should make sense for that outcome; a large dataset alone does not settle the question. section 4.3.2

Use distribution checks as part of data preparation:

  • Define a schema, then normalize or scale features where needed. section 4.3.2
  • Inspect the data for bias and anomalies. Decide whether it represents the problem, rather than relying only on automated checks. section 4.3.2
  • For LLM training, follow preparation with tokenization and embedding, then train, check, and retrain. section 4.3.2

Distribution evaluation may be a direct inspection of the data or a human-in-the-loop workflow that validates model output. The right effort depends on how confidently you can judge the data’s fit. section 4.3.2

How LLMs in Production covers it

How deep it goes
Outlines schema, normalization, inspection, and validation options without detailing pipeline implementation.
Examples
Contrasts inspecting whether data represents a problem with validating model output through a human-in-the-loop workflow.
What you can do after
Evaluate/understand whether feature distributions suit the intended task before training an LLM.
How it connects
Connects probability distributions to feature schemas, normalization, scaling, bias detection, tokenization, and embeddings.
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