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When training a language model with an overly narrow corpus, the probabilities are more likely to: 

• Do not reflect the task: The language model may not capture the nuances or requirements of the specific task it's meant to perform. For example, if the corpus used for training only consists of technical documents, the model may struggle with understanding casual language or conversational speech. 

• Do not generalize: An overly narrow corpus might lack diversity in language use, leading to a model that struggles to generalize well beyond the specific examples it was trained on. This means it may not effectively handle unseen data or scenarios that differ from the training data. 

Using a diverse and representative corpus helps to mitigate these issues by ensuring that the language model learns a broad range of language patterns and contexts, enabling it to better reflect the task at hand, generalize to new situations, and provide more intuitive and accurate predictions. 

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