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.

通过评估各种大规模语言模型的 “隐式” 检索鲁棒性，我们发现混合了黄金和干扰性上下文进行微调显著提高了模型对检索不准确性的鲁棒性，同时仍然保持着在检索准确时提取正确答案的能力，这表明大规模语言模型可以通过仅从最终答案的监督中学习来隐式处理相关或无关的检索上下文，并且引入显式相关性判断的额外过程可能是不必要的并且会打断端到端的方法。