LLM dropout is a regularization technique used in large language models to prevent overfitting during training. Dropout involves randomly dropping out (i.e., setting to zero) some of the units in the hidden layers of the model during each training iteration.
This helps to prevent the model from relying too heavily on any one feature or unit and encourages the development of more robust representations.
Dropout has been shown to be effective in improving the performance of large language models and is commonly used in conjunction with other regularization techniques such as L1 and L2 regularization.
