The input tokens are compared with the predicted tokens through Kullback-Leibler (KL) divergence loss function, shown in Eq. 2, which pairwise measure the probability distribution of the true output encoding 𝑦 !"#$ and computed one 𝑦 %"$& . The loss is backpropagated to the encoder layers of RoBERTa, the feed-forward neural network layers, and the softmax function to update the weights.