## 3 Research Methodology
### Model Fine-Tuning
#### 3.4.3 Computational Efficiency, Hardware Considerations, and Automated Hyperparameter Configuration  
To ensure accessibility and ease of replication, we opted for pre-trained models rather than open-source alternatives that require specialized computational resources. This decision allows experiments to be conducted on standard computing devices without high-end hardware requirements. All experiments were performed on a MacBook Air M1 (2021) with 8 GB RAM, an 8-Core CPU, and a 7-Core GPU, demonstrating that our methodology does not rely on dedicated GPU clusters or cloud infrastructure. This choice ensures that our approach remains practical and reproducible for researchers working with limited resources.  
To compare the computational efficiency of the models, we analyzed both fine-tuning time and cost. Mistral 7B required only three fine-tuning sessions, costing \(\xi\)4, \(\xi\)5.5, and \(\xi\)7 respectively, each taking approximately 5 min. In contrast, GPT-4 required an initial dataset loading phase lasting approximately one hour (as documented in our GitHub screenshot), with fine-tuning costs ranging from \(\xi\)7 to \(\xi\)10 per session, depending on dataset size. These results indicate that Mistral 7B offers a more cost-effective and time-efficient fine-tuning process, whereas GPT-4 requires additional time for dataset preparation but provides a more optimized API-based adaptation. These trade-offs should be considered when selecting a model for ontology engineering tasks.  
In our fine-tuning experiments with both models, we opted for an automated hyperparameter configuration rather than manual tuning. This decision was motivated by the goal of making the fine-tuning process more accessible to researchers and practitioners without deep expertise in hyperparameter optimization. By leveraging the framework's default automated settings, the model was able to adjust learning parameters dynamically based on the dataset size, training steps, and optimization objectives, eliminating the need for manual intervention. Using automated hyperparameter settings led to a more stable training process but also introduced certain limitations. The model exhibited slower initial learning improvements, as the automated configuration did not include aggressive optimization strategies such as adaptive learning rate decay or manual gradient clipping. However, the benefit of this approach was that it reduced the risk of overfitting or instability, which can sometimes occur with aggressive hyperparameter tuning. Furthermore, the consistency observed in later fine-tuning iterations suggests that automated settings provided a balanced learning strategy, allowing Mistral 7B to generalize well across domain-specific ontology structures. While this approach facilitated a smoother fine-tuning experience, future research could explore the impact of alternative hyperparameter configurations, such as manually adjusting batch sizes, learning rates, or weight decay parameters, to further optimize model performance. Our methodology remains adaptable, allowing others to experiment with different settings while maintaining the overall workflow for fine-tuning LLMs in ontology engineering.