  Retrieval-augmented generation has gained popularity as a framework to
enhance large language models with external knowledge. However, its
effectiveness hinges on the retrieval robustness of the model. If the model
lacks retrieval robustness, its performance is constrained by the accuracy of
the retriever, resulting in significant compromises when the retrieved context
is irrelevant. In this paper, we evaluate the "implicit" retrieval robustness
of various large language models, instructing them to directly output the final
answer without explicitly judging the relevance of the retrieved context. Our
findings reveal that fine-tuning on a mix of gold and distracting context
significantly enhances the model's robustness to retrieval inaccuracies, while
still maintaining its ability to extract correct answers when retrieval is
accurate. This suggests that large language models can implicitly handle
relevant or irrelevant retrieved context by learning solely from the
supervision of the final answer in an end-to-end manner. Introducing an
additional process for explicit relevance judgment can be unnecessary and
disrupts the end-to-end approach.
