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Language model

As explained in Outlier Detection in Python

A large language model is a neural model trained on very large text corpora to process and generate natural language.

The supplied evidence does not define a language model or explain how one processes or generates text. Its models instead learn predominant patterns in data for outlier detection; records that fit poorly may be flagged for investigation. section 3.2.5 section 4.4

How Outlier Detection in Python covers it

How deep it goes
Explains fitting and predictive failure in outlier detectors, but does not define language-model training or operation.
Examples
Uses breast-cancer records and a decision tree distinguishing real from synthetic data, rather than a language-model example.
What it assumes
Defines supervised labels and contrasts them with unlabeled data; assumes no language-model background.
What you can do after
Evaluate/understand model fit for outlier detection, but not build or operate a language model.
How it connects
Connects predictive models with binary classification, synthetic data, and outlier detection, not natural-language processing.

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This explainer was generated by Manning's knowledge graph from the text of Outlier Detection in Python. The section links point to the parts of the book it draws on.