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.

我们提出了一种名为GNN-Ret的新型检索方法，利用图神经网络（GNNs）考虑段落间的相关性来增强检索过程，并通过RGNN-Ret处理多跳推理问题，从而提高问题回答准确率。实验结果表明，GNN-Ret相比多次查询的强基线方法在单次查询LLM的问题回答准确率上更高，而RGNN-Ret进一步提高了准确率，在2WikiMQA数据集上的准确率提高了10.4%。

图神经网络增强的LLM问题回答检索