让大模型学会三词句式知识图谱语言，精准生成与理解事实。
MKGL: Mastery of a Three-Word Language
- 构建三词结构的专属知识图谱语言KGL，用实体+关系+实体表达事实。
- 在知识补全任务上错误率大幅低于传统嵌入方法，实现高精度生成。
- 适合需要精确事实生成与解释的场景，如智能问答、知识推理。
大型语言模型（LLMs）在自然语言处理任务中取得了显著进展，但其在知识图谱（KG）中的应用仍处于探索阶段。知识图谱以三元组形式描述事实，具有极低幻觉风险。本文提出一种专用的知识图谱语言（KGL），其句子严格由实体名词、关系动词和另一实体名词构成。尽管该语言对大模型不熟悉，我们通过定制词典、示例句子，结合实时知识图谱上下文检索与KGL词元嵌入增强，有效促进模型学习。实验表明，模型在KGL上表现出流利性，知识补全错误率显著低于传统嵌入方法。此外，增强后的模型能准确从初始实体生成三词句子，并解读知识图谱中未见过的新术语。
原文摘要 · Abstract (English)
Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs), which describe facts in the form of triplets and allow minimal hallucinations, remains an underexplored frontier. In this paper, we investigate the integration of LLMs with KGs by introducing a specialized KG Language (KGL), where a sentence precisely consists of an entity noun, a relation verb, and ends with another entity noun. Despite KGL's unfamiliar vocabulary to the LLM, we facilitate its learning through a tailored dictionary and illustrative sentences, and enhance context understanding via real-time KG context retrieval and KGL token embedding augmentation. Our results reveal that LLMs can achieve fluency in KGL, drastically reducing errors compared to conventional KG embedding methods on KG completion. Furthermore, our enhanced LLM shows exceptional competence in generating accurate three-word sentences from an initial entity and interpreting new unseen terms out of KGs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。