Large language models augmented with task-relevant documents have
demonstrated impressive performance on knowledge-intensive tasks. However,
regarding how to obtain effective documents, the existing methods are mainly
divided into two categories. One is to retrieve from an external knowledge
base, and the other is to utilize large language models to generate documents.
We propose an iterative retrieval-generation collaborative framework. It is not
only able to leverage both parametric and non-parametric knowledge, but also
helps to find the correct reasoning path through retrieval-generation
interactions, which is very important for tasks that require multi-step
reasoning. We conduct experiments on four question answering datasets,
including single-hop QA and multi-hop QA tasks. Empirical results show that our
method significantly improves the reasoning ability of large language models
and outperforms previous baselines.

我们提出了一种迭代检索 - 生成的协作框架，该框架可以利用参数化和非参数化知识，帮助找到正确的推理路径，并显著提高大型语言模型的推理能力。实验结果显示我们的方法优于之前的基线模型，在单跳和多跳问答任务上取得了显著的改进。