Prompt-Time Symbolic Knowledge Capture with Large Language Models
Tolga Çöplü Arto Bendiken Andrii Skomorokhov Eduard Bateiko Stephen Cobb Joshua J. Bouw
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.