L1L2n

L1L2n refers to a specific type of language model that incorporates both L1 and L2 regularization techniques during its training process. L1 regularization, also known as Lasso regularization, adds a penalty proportional to the absolute value of the magnitude of coefficients. This can lead to sparse solutions, where some coefficients are driven to exactly zero, effectively performing feature selection. L2 regularization, or Ridge regularization, adds a penalty proportional to the square of the magnitude of coefficients. This tends to shrink coefficients towards zero but rarely makes them exactly zero, preventing overfitting by reducing the impact of less important features.

By combining L1 and L2 regularization, often referred to as Elastic Net regularization, L1L2n models aim to leverage the benefits of both. This hybrid approach can achieve sparsity like L1 regularization while also providing the stability and shrinkage of L2 regularization. This can be particularly useful in scenarios with high-dimensional data where a large number of features are present, and it's desirable to identify a subset of the most influential features while simultaneously preventing model overfitting. The specific weighting between the L1 and L2 penalties is a hyperparameter that needs to be tuned during model development. This tuning process typically involves cross-validation to find the optimal balance that yields the best performance on unseen data.