Overfitting and underfitting are common challenges in training large language models, and can be addressed using various techniques, including:

Regularization : Regularization is a technique that adds a penalty term to the loss function, which helps prevent overfitting by reducing the complexity of the model. L1 and L2 regularization are common techniques used in large language models.

Dropout : Dropout is another regularization technique that randomly drops out some of the neurons during training, which reduces the interdependence of the neurons and prevents overfitting.

Early stopping : Early stopping is a technique that stops the training process when the performance on a validation set stops improving. This helps prevent overfitting by avoiding the model from continuing to learn the noise in the training data.

Increasing data : One of the most effective ways to prevent overfitting is by increasing the size of the training data. This helps the model to generalize better by learning from more examples.

Hyperparameter tuning : Hyperparameters such as the learning rate, batch size, and number of epochs can have a significant impact on the performance of the model. By tuning these hyperparameters, you can find the optimal values that prevent overfitting and underfitting.