3. Understanding language model optimization
After the training period, it is often necessary to optimize the model to improve its performance on specific tasks. Here again, there are several approaches. Here are two representative examples.
3.1 Fine tuning
Fine tuning involves adjusting a pre-trained model to additional data or data specific to a given task (e.g. text translation or classification).
This method is used to adapt a model to a particular context without having to re-train it entirely. For example, GPT which is initially trained on a large generalist corpus can be refined on a specific dataset to excel in a task such as medical report writing.
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Understanding language model optimization
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