In the field of Question Answering (QA), unifying large language models
(LLMs) with external databases has shown great success. However, these methods
often fall short in providing the advanced reasoning needed for complex QA
tasks. To address these issues, we improve over a novel approach called
Knowledge Graph Prompting (KGP), which combines knowledge graphs with a
LLM-based agent to improve reasoning and search accuracy. Nevertheless, the
original KGP framework necessitates costly fine-tuning with large datasets yet
still suffers from LLM hallucination. Therefore, we propose a reasoning-infused
LLM agent to enhance this framework. This agent mimics human curiosity to ask
follow-up questions to more efficiently navigate the search. This simple
modification significantly boosts the LLM performance in QA tasks without the
high costs and latency associated with the initial KGP framework. Our ultimate
goal is to further develop this approach, leading to more accurate, faster, and
cost-effective solutions in the QA domain.

在问答领域中，将大型语言模型与外部数据库结合已经取得了巨大的成功。然而，这些方法在提供复杂问答任务所需的高级推理方面常常效果不佳。为了解决这些问题，我们改进了一种称为 “知识图谱提示” 的新方法，该方法结合了知识图谱和基于大型语言模型的代理以提高推理和搜索准确性。然而，原始的知识图谱提示框架需要在大型数据集上进行昂贵的微调，并且仍然存在着大型语言模型幻觉的问题。因此，我们提出了一种注入推理能力的大型语言模型代理来增强该框架。这种代理模仿人类的好奇心来提问后续问题，以更高效地导航搜索。这种简单的改进显著提升了大型语言模型在问答任务中的性能，而无需承担初始 “知识图谱提示” 框架的高成本和延迟。我们的最终目标是进一步发展这种方法，在问答领域提供更准确、更快速、更具成本效益的解决方案。

CuriousLLM: 提升多文档问答与推理注入的知识图提示

CuriousLLM: Elevating Multi-Document QA with Reasoning-Infused Knowledge  Graph Prompting

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。