  Retrieval augmented generation has revolutionized large language model (LLM)
outputs by providing factual supports. Nevertheless, it struggles to capture
all the necessary knowledge for complex reasoning questions. Existing retrieval
methods typically divide reference documents into passages, treating them in
isolation. These passages, however, are often interrelated, such as passages
that are contiguous or share the same keywords. Therefore, recognizing the
relatedness is crucial for enhancing the retrieval process. In this paper, we
propose a novel retrieval method, called GNN-Ret, which leverages graph neural
networks (GNNs) to enhance retrieval by considering the relatedness between
passages. Specifically, we first construct a graph of passages by connecting
passages that are structure-related and keyword-related. A graph neural network
(GNN) is then leveraged to exploit the relationships between passages and
improve the retrieval of supporting passages. Furthermore, we extend our method
to handle multi-hop reasoning questions using a recurrent graph neural network
(RGNN), named RGNN-Ret. At each step, RGNN-Ret integrates the graphs of
passages from previous steps, thereby enhancing the retrieval of supporting
passages. Extensive experiments on benchmark datasets demonstrate that GNN-Ret
achieves higher accuracy for question answering with a single query of LLMs
than strong baselines that require multiple queries, and RGNN-Ret further
improves accuracy and achieves state-of-the-art performance, with up to 10.4%
accuracy improvement on the 2WikiMQA dataset.
