Multi-Hop Question Answering (MHQA) tasks present a significant challenge for large language models (LLMs) due to the intensive knowledge required. Current solutions, like Retrieval-Augmented Generation, typically retrieve potential documents from an external corpus to read an answer. However, the performance of this retrieve-then-read paradigm is constrained by the retriever and the inevitable noise in the retrieved documents. To mitigate these challenges, we introduce a novel generate-then-ground (GenGround) framework, synergizing the parametric knowledge of LLMs and external documents to solve a multi-hop question. GenGround empowers LLMs to alternate two phases until the final answer is derived: (1) formulate a simpler, single-hop question and directly generate the answer; (2) ground the question-answer pair in retrieved documents, amending any wrong predictions in the answer. We also propose an instructional grounding distillation method to generalize our method into smaller models. Extensive experiments conducted on four datasets illustrate the superiority of our method.

多跳问题回答 (MHQA) 任务对于大型语言模型 (LLMs) 来说是一项重大挑战。我们提出了一个新颖的生成然后固定 (GenGround) 框架，结合LLMs的参数化知识和外部文档，以解决多跳问题。通过在检索的文档中确认问题 - 答案对，我们的方法在四个数据集上展示了其优越性。

多跳问题回答中的生成先然后建模的检索增强生成