Retrieval-Generation Synergy Augmented Large Language Models
Zhangyin Feng Xiaocheng Feng Dezhi Zhao Maojin Yang Bing Qin
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.