Retrieval-augmented generation (RAG) offers an effective approach for addressing question answering (QA) tasks. However, the imperfections of the retrievers in RAG models often result in the retrieval of irrelevant information, which could introduce noises and degrade the performance, especially when handling multi-hop questions that require multiple steps of reasoning. To enhance the multi-hop reasoning ability of RAG models, we propose TRACE. TRACE constructs knowledge-grounded reasoning chains, which are a series of logically connected knowledge triples, to identify and integrate supporting evidence from the retrieved documents for answering questions. Specifically, TRACE employs a KG Generator to create a knowledge graph (KG) from the retrieved documents, and then uses an Autoregressive Reasoning Chain Constructor to build reasoning chains. Experimental results on three multi-hop QA datasets show that TRACE achieves an average performance improvement of up to 14.03% compared to using all the retrieved documents. Moreover, the results indicate that using reasoning chains as context, rather than the entire documents, is often sufficient to correctly answer questions.

翻译：检索增强生成（RAG）为解决问答任务提供了一种有效方法。然而，RAG模型中检索器的不完善常常导致检索到不相关信息，这可能引入噪声并降低性能，尤其是在处理需要多步推理的多跳问题时。为了增强RAG模型的多跳推理能力，我们提出了TRACE方法。TRACE构建基于知识的推理链——一系列逻辑上相互关联的知识三元组——以识别并整合检索文档中的支持证据来回答问题。具体而言，TRACE利用知识图谱生成器从检索文档中创建知识图谱，然后使用自回归推理链构建器来构建推理链。在三个多跳问答数据集上的实验结果表明，与直接使用所有检索文档相比，TRACE实现了平均高达14.03%的性能提升。此外，结果还表明，使用推理链而非完整文档作为上下文，通常足以正确回答问题。