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…
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Taxonomy
MethodsPathways Language Model
