Validation

Validation Data Set

The validation data set is a crucial component in the training of Small Language Models (SLMs). It is used to evaluate the performance of the trained model and tune its hyperparameters. The validation data set should follow the same probability distribution as the training data set and is independent of the data used for training the model.

Validation Process

The validation process typically involves the following steps:

• Training the model on the training data set using a supervised learning method, such as gradient descent or stochastic gradient descent.

• Evaluating the trained model's performance on the validation data set to compare different candidate models or hyperparameters.

• Selecting the model with the best performance on the validation data set.

• Confirming the selected model's performance on a separate test data set to avoid overfitting to the validation set.

Validation Techniques

Hold-out method 

A portion of the training data is held out as the validation set, and the model's performance is evaluated on this set.

Cross-validation

The training data is divided into k folds, and the model is trained k times, each time using a different fold as the validation set.

Early stopping

Training is stopped when the error on the validation set starts to increase, indicating overfitting to the training data.

Validation in SLM Training

In the context of SLM training, the validation data set is used to:

• Tune hyperparameters such as the learning rate, batch size, and model architecture.

• Monitor for overfitting during the training process.

• Select the best-performing model among multiple training runs or model variants.

By incorporating a robust validation process, researchers can ensure that the trained SLM generalizes well to unseen data and maintains its performance in real-world applications. 10, 11, 12