Augmenting large language models (LLMs) with user-specific knowledge is crucial for real-world applications, such as personal AI assistants. However, LLMs inherently lack mechanisms for prompt-driven knowledge capture. This paper investigates utilizing the existing LLM capabilities to enable prompt-driven knowledge capture, with a particular emphasis on knowledge graphs. We address this challenge by focusing on prompt-to-triple (P2T) generation. We explore three methods: zero-shot prompting, few-shot prompting, and fine-tuning, and then assess their performance via a specialized synthetic dataset. Our code and datasets are publicly available at https://github.com/HaltiaAI/paper-PTSKC.

翻译：为大型语言模型（LLMs）补充用户特定知识对于实际应用（如个人AI助手）至关重要。然而，LLMs天生缺乏用于提示驱动知识捕获的机制。本文研究如何利用现有LLM能力实现提示驱动知识捕获，特别关注知识图谱。我们聚焦于提示到三元组（P2T）生成来解决这一挑战。我们探索了三种方法：零样本提示、少样本提示和微调，并通过专门的合成数据集评估其性能。我们的代码和数据集公开于 https://github.com/HaltiaAI/paper-PTSKC。