OntoChatGPT Information System: Ontology-Driven Structured Prompts for ChatGPT Meta-Learning

TL;DR

This paper introduces an ontology-driven structured prompt methodology for ChatGPT that enhances its meta-learning capabilities, demonstrated through a Ukrainian rehabilitation domain chatbot, and applicable to other LLM-based systems.

Contribution

It develops formal models and a comprehensive methodology for integrating ontology-driven prompts with ChatGPT's meta-learning, improving chatbot performance across languages and domains.

Findings

Effective entity extraction and classification in Ukrainian rehabilitation context

Enhanced response relevance through ontology-driven prompts

Versatile approach applicable to various LLM-based chatbots

Abstract

This research presents a comprehensive methodology for utilizing an ontology-driven structured prompts system in interplay with ChatGPT, a widely used large language model (LLM). The study develops formal models, both information and functional, and establishes the methodological foundations for integrating ontology-driven prompts with ChatGPT's meta-learning capabilities. The resulting productive triad comprises the methodological foundations, advanced information technology, and the OntoChatGPT system, which collectively enhance the effectiveness and performance of chatbot systems. The implementation of this technology is demonstrated using the Ukrainian language within the domain of rehabilitation. By applying the proposed methodology, the OntoChatGPT system effectively extracts entities from contexts, classifies them, and generates relevant responses. The study highlights the versatility of the methodology, emphasizing its applicability not only to ChatGPT but also to other chatbot systems based on LLMs, such as Google's Bard utilizing the PaLM 2 LLM. The underlying principles of meta-learning, structured prompts, and ontology-driven information retrieval form the core of the proposed methodology, enabling their adaptation and utilization in various LLM-based systems. This versatile approach opens up new possibilities for NLP and dialogue systems, empowering developers to enhance the performance and functionality of chatbot systems across different domains and languages.