Thelimited datainbatchRLproduces inherent uncertainty in value estimates of states/actions that were insufficiently represented in the training data.

In this work, we introduce ChatQA, a suite of models that outperform GPT -4 on retrieval-augmented generation (RAG) and conversational question answering (QA).

Transformer-based models, which have recently seen a surge in popularity due to their good performance and applicability to a variety of tasks, have a similar problem.

Finally, we describe the training setup for our back-translation experiments. We continue to differentiate our method from other existing works. Our method does not train multiple peer models with EM training either. In each round, a forward (or backward) model takes turn to play the "back-translation" role to train The role is switched in the next round. In other words, source and target are identical.