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。