Prompt-Time Symbolic Knowledge Capture with Large Language Models

Tolga Çöplü, Arto Bendiken, Andrii Skomorokhov, Eduard Bateiko, Stephen Cobb +1 more
2/1/2024
cs.CLcs.AI

Official code: HaltiaAI/paper-PTSKC (7 stars, last commit 2024-02-29).

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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HaltiaAI/paper-PTSKCOfficial100%

This repository contains code and access to the dataset used for our paper titled `Prompt-Time Symbolic Knowledge Capture with Large Language Models`

71PythonFeb 1, 20242 years ago

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Cite this paper

@article{pl2024prompttime,
  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},
  year   = {2024},
  eprint = {2402.00414},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.00414v1}
}

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