Challenges in training LLMs – overfitting, underfitting, and more

Training LLMs presents several challenges that can affect the quality and applicability of the resulting models. Overfitting and underfitting are two primary concerns, along with several others.

Overfitting occurs when an LLM learns the training data too well, including its noise and outliers. This typically happens when the model is too complex relative to the simplicity of the data or when it has been trained for too long. An overfitted model performs well on its training data but poorly on new, unseen data because it fails to generalize the underlying patterns appropriately. To combat overfitting, techniques such as introducing dropout layers, applying regularization, and using early stopping during training are employed. Data augmentation and ensuring a large and diverse training set can also prevent the model from learning the training data too closely.

Underfitting is the opposite problem, where the...