Although the method of enhancing large language models' (LLMs') reasoning
ability and reducing their hallucinations through the use of knowledge graphs
(KGs) has received widespread attention, the exploration of how to enable LLMs
to integrate the structured knowledge in KGs on-the-fly remains inadequate.
Researchers often co-train KG embeddings and LLM parameters to equip LLMs with
the ability of comprehending KG knowledge. However, this resource-hungry
training paradigm significantly increases the model learning cost and is also
unsuitable for non-open-source, black-box LLMs. In this paper, we employ
complex question answering (CQA) as a task to assess the LLM's ability of
comprehending KG knowledge. We conducted a comprehensive comparison of KG
knowledge injection methods (from triples to natural language text), aiming to
explore the optimal prompting method for supplying KG knowledge to LLMs,
thereby enhancing their comprehension of KG. Contrary to our initial
expectations, our analysis revealed that LLMs effectively handle messy, noisy,
and linearized KG knowledge, outperforming methods that employ well-designed
natural language (NL) textual prompts. This counter-intuitive finding provides
substantial insights for future research on LLMs' comprehension of structured
knowledge.

我们通过复杂问题回答作为任务，对比了不同的知识图谱注入方法，旨在探索最佳提供知识图谱给大型语言模型的提示方法，从而增强其对知识图谱的理解能力。与预期相反，我们的分析发现，大型语言模型能够有效处理混乱、噪音和线性化的知识图谱知识，并且表现优于使用设计良好的自然语言文本提示的方法。这一反直觉的发现为未来关于大型语言模型理解结构化知识的研究提供了重要见解。