The 'pre-train, prompt, predict' paradigm of large language models (LLMs) has
achieved remarkable success in open-domain question answering (OD-QA). However,
few works explore this paradigm in the scenario of multi-document question
answering (MD-QA), a task demanding a thorough understanding of the logical
associations among the contents and structures of different documents. To fill
this crucial gap, we propose a Knowledge Graph Prompting (KGP) method to
formulate the right context in prompting LLMs for MD-QA, which consists of a
graph construction module and a graph traversal module. For graph construction,
we create a knowledge graph (KG) over multiple documents with nodes symbolizing
passages or document structures (e.g., pages/tables), and edges denoting the
semantic/lexical similarity between passages or intra-document structural
relations. For graph traversal, we design an LM-guided graph traverser that
navigates across nodes and gathers supporting passages assisting LLMs in MD-QA.
The constructed graph serves as the global ruler that regulates the
transitional space among passages and reduces retrieval latency. Concurrently,
the LM-guided traverser acts as a local navigator that gathers pertinent
context to progressively approach the question and guarantee retrieval quality.
Extensive experiments underscore the efficacy of KGP for MD-QA, signifying the
potential of leveraging graphs in enhancing the prompt design for LLMs. Our
code is at this https URL

在多文档问答（MD-QA）方案中，使用知识图谱引导大型语言模型进行提示设计的带有图构造和遍历模块的知识图谱提示（KGP）方法，在提取支持材料和减少检索延迟方面取得了显著效果，并显示出利用图形增强大型语言模型中的提问设计的潜力。