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Book Overview & Buying
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Table Of Contents
LLMs in Enterprise
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Data partitioning involves dividing the annotated dataset into three primary subsets: training, validation, and testing. Each of these subsets plays a distinct role in the model development process. The training set is used to teach the model patterns and relationships within the data, the validation set ensures optimal tuning of model parameters, and the testing set evaluates the model’s performance on unseen data. Effective partitioning is essential to prevent overfitting, underfitting, or data leakage, which can compromise the reliability and accuracy of the LLM.
For example, in time-series data, splitting needs to adhere to the temporal order—training on earlier data and validating and testing on later data. When this order is disrupted, the model may have access to information from the future while training, causing data leakage and a falsely improved performance. Correct splitting techniques such as these enable the model to generalize well...