Recent work shown the capability of Large Language Models (LLMs) to solve
tasks related to Knowledge Graphs, such as Knowledge Graph Completion, even in
Zero- or Few-Shot paradigms. However, they are known to hallucinate answers, or
output results in a non-deterministic manner, thus leading to wrongly reasoned
responses, even if they satisfy the user's demands. To highlight opportunities
and challenges in knowledge graphs-related tasks, we experiment with two
distinguished LLMs, namely Mixtral-8x7B-Instruct-v0.1, and gpt-3.5-turbo-0125,
on Knowledge Graph Completion for static knowledge graphs, using prompts
constructed following the TELeR taxonomy, in Zero- and One-Shot contexts, on a
Task-Oriented Dialogue system use case. When evaluated using both strict and
flexible metrics measurement manners, our results show that LLMs could be fit
for such a task if prompts encapsulate sufficient information and relevant
examples.

大型语言模型 (LLMs) 能够解决与知识图谱相关的任务，如知识图谱补全，尤其在零次或少次训练的范式下。然而，它们被称为会产生错误的答案，或以不确定的方式输出结果，从而导致错误推理的回答，即使它们满足用户的需求。为了强调知识图谱相关任务中的机遇和挑战，我们在静态知识图谱的知识图谱补全任务中，使用 TELeR 分类法构建的提示，在零次和一次迭代的上下文中，通过两个显著的 LLMs 进行实验，即 Mixtral-8x7B-Instruct-v0.1 和 gpt-3.5-turbo-0125，在以任务为导向的对话系统使用案例中。当使用严格和灵活的度量标准进行评估时，我们的结果表明，如果提示包含足够的信息和相关示例，LLMs 可能适用于这样的任务。