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.

翻译：本研究提出了一套利用本体驱动的结构化提示系统与广泛使用的大语言模型（LLM）ChatGPT协同作用的综合方法论。研究构建了信息模型与功能模型等正式模型，并为将本体驱动提示与ChatGPT的元学习能力相结合奠定了方法论基础。由此产生的三要素成果包括方法论基础、先进信息技术及OntoChatGPT系统，三者共同提升了聊天机器人系统的效能与性能。该技术的实现以乌克兰语在康复领域中的应用为例进行展示。通过应用所提方法论，OntoChatGPT系统能够有效从上下文中提取实体、对其进行分类并生成相关响应。研究强调了方法论的通用性，指出其不仅适用于ChatGPT，还可推广至其他基于LLM的聊天机器人系统（如采用PaLM 2 LLM的Google Bard）。元学习、结构化提示及本体驱动信息检索的基本原理构成了所提方法论的核心，使其能够适应并应用于各类LLM系统。这种通用方法为自然语言处理与对话系统开辟了新可能，使开发者能够跨不同领域与语言提升聊天机器人系统的性能与功能。