Retrieval-augmented generation (RAG) has significantly advanced large language models (LLMs) by enabling dynamic information retrieval to mitigate knowledge gaps and hallucinations in generated content. However, these systems often falter with complex reasoning and consistency across diverse queries. In this work, we present Think-on-Graph 2.0, an enhanced RAG framework that aligns questions with the knowledge graph and uses it as a navigational tool, which deepens and refines the RAG paradigm for information collection and integration. The KG-guided navigation fosters deep and long-range associations to uphold logical consistency and optimize the scope of retrieval for precision and interoperability. In conjunction, factual consistency can be better ensured through semantic similarity guided by precise directives. ToG${2.0}$ not only improves the accuracy and reliability of LLMs' responses but also demonstrates the potential of hybrid structured knowledge systems to significantly advance LLM reasoning, aligning it closer to human-like performance. We conducted extensive experiments on four public datasets to demonstrate the advantages of our method compared to the baseline.

翻译：检索增强生成（RAG）通过实现动态信息检索，显著提升了大语言模型（LLMs）的性能，以弥补生成内容中的知识缺口并减少幻觉。然而，这些系统在处理复杂推理和确保跨多样查询的一致性方面常常表现不佳。本研究提出了Think-on-Graph 2.0，一个增强的RAG框架，它将问题与知识图谱对齐，并将其用作导航工具，从而深化和完善了用于信息收集与整合的RAG范式。知识图谱引导的导航促进了深度和长程关联，以维护逻辑一致性，并优化检索范围以实现精确性和互操作性。同时，通过精确指令引导的语义相似性，可以更好地确保事实一致性。ToG${2.0}$不仅提高了LLMs响应的准确性和可靠性，还展示了混合结构化知识系统在显著推进LLM推理方面的潜力，使其更接近类人的性能。我们在四个公共数据集上进行了广泛的实验，以证明我们的方法相较于基线的优势。