## 2 Related Work  
### LLMs and OE  
[PERSON] et al. [1] introduce a novel framework to streamline the process of OE (OE) through conversational interactions with a language model. Their study tackles the inherent complexities and resource-intensive nature of traditional OE, particularly in projects involving stakeholders from diverse backgrounds. OntoChat aims to mitigate systematic ambiguities and biases by facilitating requirement elicitation, analysis, and testing phases through a conversational AI interface. The framework enables users to create user stories and extract competency questions by interacting with a conversational agent, significantly reducing the manual effort typically required in these initial stages. The use of large language models (LLMs) in OntoChat allows for nuanced understanding and processing of natural language, enhancing the generation and refinement of ontology requirements. The paper details an evaluation of OntoChat using the Music Meta Ontology, demonstrating its effectiveness in improving the efficiency and accuracy of OE tasks. This approach not only streamlines the OE process but also promises to enhance the accessibility and adaptability of ontologies in various domains by incorporating advanced AI-driven methods. [PERSON] et al. [2] detail the evolution and application of OE methods in the context of large language models (LLMs), particularly focusing on human-to-machine centered methodologies. The paper discusses the gradual reduction in human involvement and corresponding increase in machine automation in the process of OE. This shift aims to leverage the efficiency of LLMs while retaining essential human oversight. The proposed spectrum of collaboration ranges from fully human-driven to completely LLM-driven OE methodologies. Through systematic experimentation across various levels of human and machine collaboration, the research evaluates the effectiveness of LLMs in creating ontologically sound structures with minimal human input. However, it is important to note that in this work, fine-tuning is not researched or evaluated; the study focuses solely on simple prompting techniques. The study provides a compelling look into how LLMs can significantly augment the OE process, making it faster and potentially more accurate while still needing human expertise for critical assessments and adjustments. [PERSON] et al. [3] provide a detailed examination of how LLMs are applied in the domain of OE (OE). The paper categorizes various OE tasks that can benefit from LLMs, utilizing the Linked Open Terms (LOT) methodology as a framework for analysis. This research identifies the core phases of OE such as requirement specification, implementation, and maintenance, and highlights how LLMs are currently utilized within these areas. The authors discuss the diversity in task definitions and the need for standardized benchmarks and evaluation frameworks to measure LLM performance effectively in OE tasks. Through their analysis, [PERSON] et al. reveal significant gaps and suggest potential areas for further integration of LLMs to enhance the efficiency and effectiveness of OE processes. [PERSON] et al. [4] discuss the development of the Artificial Intelligence Ontology (AIO), which organizes and defines AI concepts to support standardization and understanding in AI research. Developed at Lawrence Berkeley National Laboratory, the AIO employs LLMs to enhance manual curation, ensuring the ontology remains current with rapid advancements in AI technology. AIO is structured around six principal categories including Networks, Layers, and Bias, addressing both technical aspects and ethical considerations of AI. This approach not only facilitates modular composition of AI methodologies but also aids in navigating the ethical landscapes of AI applications, proving to be a vital resource for AI researchers and developers. [PERSON] et al. [5] investigate the potential of LLMs to assist in the development of ontologies by generating OWL outputs directly from ontological requirements.