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Prompt-Time Symbolic Knowledge Capture with Large Language Models
- Publication Year :
- 2024
-
Abstract
- 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.<br />Comment: 8 pages, 5 figures, 1 table preprint. Under review
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2402.00414
- Document Type :
- Working Paper