Recent studies have demonstrated that large language models can perform various knowledge graph-related tasks, including knowledge graph construction, even in zero- and few-shot settings. However, they are prone to hallucinating information and producing non-deterministic outputs, which can result in flawed reasoning, even when the answers appear to meet user expectations. This unpredictability limits their integration into automated natural language processing pipelines, such as those used in chatbots or task-oriented dialogue systems. To explore the potential and limitations of large language models in knowledge graph tasks, we evaluate three prominent models, namely Mixtral-8x7b-Instruct-v0.1, GPT-3.5-Turbo-0125, and GPT-4o, on constructing static knowledge graphs. Our approach uses prompts based on the TELeR taxonomy in zero- and one-shot scenarios, within the context of a task-oriented dialogue system. Additionally, we propose a flexible evaluation framework that captures all usable information generated by the models, alongside traditional strict metrics, and introduce TODSet, a dataset tailored to gauge the performance of large language models on knowledge graph-related tasks. Our findings suggest that, with well-designed prompts containing sufficient detail and examples, large language models can effectively contribute to knowledge graph construction tasks.
Iga et al. (Thu,) studied this question.