OntoChatGPT Information System: Ontology-Driven Structured Prompts for
  ChatGPT Meta-Learning
Oleksandr Palagin Vladislav Kaverinskiy Anna Litvin Kyrylo Malakhov
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.