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.
@article{arxiv.2402.00414,
title = {Prompt-Time Symbolic Knowledge Capture with Large Language Models},
author = {Tolga Çöplü and Arto Bendiken and Andrii Skomorokhov and Eduard Bateiko and Stephen Cobb and Joshua J. Bouw},
journal= {arXiv preprint arXiv:2402.00414},
year = {2024}
}
Comments
8 pages, 5 figures, 1 table preprint. Under review