How retrieval loss is calculated in RAG model?
I was reading the code for RAG (Retrieval Augmented Generation) on transformers github.I wanted to know how gradients are backpropagated till query encoder model.I wrote an answer for it.But then I wondered… how come loss of retrieval model (query encoder) is calculated by simply taking the softmax over the doc_scores?HereI get that they are adding the softmax over doc_scores to the seq_logits loss in order backpropagate the gradients. But I am unable to understand the intuition behind doing a softmax over doc_scores? and why that prob distribution is added to seq_logits loss? Both are differentimage939×356 65.6 KB