## 3 Research Methodology
### Model Fine-Tuning
#### 3.4.2. Mistral 7b Fine-Tuning  
Fine-tuning Mistral 7B, while similar in goal to GPT-4, involves different procedural steps (Figure 2) due to its distinct system capabilities:  
1. Initial Setup: Each fine-tuning session with Mistral 7B starts with the basic Mistral model. Unlike GPT-4, Mistral does not support sequential fine-tuning on top of previously adjusted models. Therefore, each new fine-tuning session requires uploading not just the new dataset, but all previous datasets used in earlier sessions. This repetitive data loading is necessary to maintain continuity and consistency in the model's learning process. 2. Lack of Parallel Processing Capabilities: Mistral 7B does not have the capability to run parallel prompts or utilize concurrent processing with other pre-trained models. Each prompt must be processed sequentially. Initially, the basic Mistral model runs the prompt to establish a baseline before the fine-tuned versions are applied. This sequential approach ensures that each version of the model is strictly aligned with the incremental improvements intended through the fine-tuning. 3. Data Re-Upload for Each Session: For Mistral, each fine-tuning session is mainly standalone. This necessitates re-uploading all historical datasets for each new round of fine-tuning, a requirement that ensures the model does not lose the context or learning from previous sessions but does add to the setup time and computational load. These detailed fine-tuning processes for GPT-4 and Mistral 7B showcase the nuanced approaches required to optimize each model's capabilities within the specific context of OE. GPT-4's ability to build upon previous enhancements and run parallel processing contrasts with Mistral 7B's need for repeated dataset uploads and sequential processing, illustrating the operational differences between these advanced LLM platforms. In our previous research [7], we have presented the SimX-HCOME novel OE methodology, achieving substantial improvements in collaborative OE. Given these positive outcomes, we are reapplying SimX-HCOME to further refine and validate its effectiveness in automating and enhancing the OE process. This approach continues to leverage role-playing simulations among key stakeholders [17]--knowledge workers (KWs), domain  
Figure 1: The process flow for fine-tuning the basic GPT-4 model for OE. The approach involves creating datasets from segmented PDFs, fine-tuning the model iteratively with domain-specific data, generating ontologies using a one-shoot prompt, and evaluating outputs through metrics. experts (DEs), and knowledge engineers (KEs)--to streamline the development and verification of ontologies with reduced human intervention and increased accuracy. In the context of fine-tuning large language models (LLMs), such as GPT-4 (OpenAI Platform) and Mistral 7B (Mistral AI Le Platform), we opted to use an online platform that operates on a token-based payment system instead of conducting the fine-tuning locally. This decision was driven by several strategic considerations that align with the goals of accessibility, ease of use, and computational efficiency. Local fine-tuning requires substantial computational resources, often necessitating advanced GPUs, which can be prohibitively expensive and technically demanding. Online platforms mitigate this by offering access to state-of-the-art computing power on a pay-as-you-go basis, making it more cost-effective and avoiding the need for significant upfront investments in hardware. Moreover, these platforms are accessible to users without deep programming knowledge, simplifying the process through user-friendly interfaces and providing resources like pre-built scripts and comprehensive documentation. This democratizes access to advanced tools, allowing users from various backgrounds to experiment with and deploy AI solutions tailored to their needs. The community support and professional assistance available on these platforms also make them an invaluable resource for both novice and experienced users.