Related papers: Prompt-Time Symbolic Knowledge Capture with Large Language Models

Prompt-Time Symbolic Knowledge Capture with Large Language Models

URL: http://arxiv.org/abs/2402.00414v1
Date: Thu, 1 Feb 2024 08:15:28 GMT
Title: Prompt-Time Symbolic Knowledge Capture with Large Language Models
Authors: Tolga \c{C}\"opl\"u, Arto Bendiken, Andrii Skomorokhov, Eduard Bateiko, Stephen Cobb, Joshua J. Bouw (Haltia, Inc.)
Abstract summary: Augmenting large language models (LLMs) with user-specific knowledge is crucial for real-world applications, such as personal AI assistants. This paper investigates utilizing the existing LLM capabilities to enable prompt-driven knowledge capture.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
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.

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