用知识图谱增强大模型推理，减少幻觉，提升准确性。
Thinking with Knowledge Graphs: Enhancing LLM Reasoning Through Structured Data
- 将知识图谱转化为编程语言格式，融入大模型表示
- 在复杂推理任务中显著提升性能，降低错误率
- 适合需要可解释推理的AI应用开发者
大型语言模型（LLMs）在自然语言理解与生成方面表现出色，但在复杂推理任务中常出现幻觉问题。近期研究显示，利用知识图谱（KGs）可有效提升其表现。本工作提出将知识图谱结构与语义通过编程语言形式紧密融入大模型表征中。实验表明，该方法显著提升了大模型在复杂推理场景中的性能，并使推理过程可被知识图谱所约束和验证。我们是首个将知识图谱以编程语言形式表示并用于微调预训练大模型的研究。该融合机制推动了大模型更准确、可解释的推理能力发展。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, they often struggle with complex reasoning tasks and are prone to hallucination. Recent research has shown promising results in leveraging knowledge graphs (KGs) to enhance LLM performance. KGs provide a structured representation of entities and their relationships, offering a rich source of information that can enhance the reasoning capabilities of LLMs. For this work, we have developed different techniques that tightly integrate KG structures and semantics into LLM representations. Our results show that we are able to significantly improve the performance of LLMs in complex reasoning scenarios, and ground the reasoning process with KGs. We are the first to represent KGs with programming language and fine-tune pretrained LLMs with KGs. This integration facilitates more accurate and interpretable reasoning processes, paving the way for more advanced reasoning capabilities of LLMs.
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