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LLMs 利用图增强推理和知识整合能力

研究人员正在探索通过将大型语言模型(LLMs)与图结构集成来增强其推理能力的新方法。“Visual Graph Scaffolds”提出使用图作为内部推理辅助工具,类似于人类的心智图,在视觉呈现时效果显著,但在展平为文本时效果较差。另一种方法“Code-on-Graph”生成基于知识图谱模式的可执行代码,以实现灵活且可扩展的程序化推理,在多个基准测试中表现优于先前的方法。一种相关技术“Search-on-Graph”允许 LLMs 通过根据推理历史和可用结构选择关系来直接导航知识图谱,在没有针对特定任务进行微调的情况下表现出色。 AI

影响 这些方法旨在提高 LLM 的推理和知识整合能力,有望为复杂任务带来更准确、更可靠的 AI 系统。

排序理由 多篇研究论文提出了将 LLMs 与图结构集成以改进推理的新方法。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →

LLMs 利用图增强推理和知识整合能力

报道来源 [4]

  1. arXiv cs.AI TIER_1 English(EN) · Runlin Lei, Xiaokui Xiao, Zhewei Wei ·

    大型语言模型结构化推理的视觉图脚手架

    arXiv:2606.02673v1 Announce Type: new Abstract: Graphs have been used to enhance large language models (LLMs) for structured reasoning, mostly as external knowledge sources are provided to models at test time. In this paper, we take a different view: the value of graphs for LLMs …

  2. arXiv cs.AI TIER_1 English(EN) · Weiwei Ding, Zixuan Li, Long Bai, Zhuo Chen, Kun Su, Fei Wang, Xiaolong Jin, Jin Zhang, Jiafeng Guo, Xueqi Cheng ·

    Code-on-Graph:基于知识图谱的大语言模型进行迭代式程序化推理

    arXiv:2606.03705v1 Announce Type: new Abstract: Knowledge Graphs (KGs) are widely used to mitigate the limitations of Large Language Models (LLMs), such as outdated knowledge and hallucinations. Existing LLM-KG integration frameworks typically rely on predefined operators to retr…

  3. arXiv cs.AI TIER_1 English(EN) · Xueqi Cheng ·

    Code-on-Graph:基于知识图谱的大语言模型进行迭代式程序化推理

    Knowledge Graphs (KGs) are widely used to mitigate the limitations of Large Language Models (LLMs), such as outdated knowledge and hallucinations. Existing LLM-KG integration frameworks typically rely on predefined operators to retrieve factual knowledge from KGs and inject it in…

  4. arXiv cs.CL TIER_1 English(EN) · Jia Ao Sun, Hao Yu, Fabrizio Gotti, Fengran Mo, Yihong Wu, Yuchen Hui, Zhan Su, Lingfeng Xiao, Jian-Yun Nie ·

    Search-on-Graph:知识图谱上大型语言模型推理的迭代式信息导航

    arXiv:2510.08825v2 Announce Type: replace Abstract: Large language models (LLMs) augmented with knowledge graphs (KGs) offer a promising approach for knowledge-intensive reasoning. Central to this approach is the selection of appropriate reasoning paths in the KG. Yet, existing m…