## 4 Experiments and Results
### Experiments  
Overall, the results indicated that the open-source models performed comparably to GPT-4, especially in grammar and contextual understanding. While GPT-4 remained slightly ahead in consistency across all categories, open-source models such as Owen2-7B and \(\text{orca\_mini\_v7\_7b}\) performed similarly in our experiments, showing potential for GDPR compliance tasks. The performance differences between the models highlighted specific areas where open-source models excelled and where further tuning may be required to fully meet GDPR requirements. **Experiment 1--basic performance**. Experiment 1 established a baseline for out-of-the-box capabilities. The experiment assessed the natural ability of the models to generate GDPR-compliant data categories, without specialized prompts. Here, all models, including the open-source options, performed well in grammar, structure, and contextual understanding, with Qwen2-7B and GPT-4 achieving the highest scores. The competitive \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Experiment**} & \multirow{2}{*}{**Latency**} & \multicolumn{2}{c}{**Structure and Style**} & \multirow{2}{*}{**Grammar**} & \multirow{2}{*}{**Validity**} & \multicolumn{2}{c}{**Contextual Understanding**} \\ \hline \multirow{3}{*}{**Qwen2-7B**} & Exp 1 & 5 & 5 & 5 & 5 & 5 \\  & Exp 2 & 0 & 4 & 5 & 5 & 4 \\  & Exp 3 & 5 & 5 & 5 & 4 & 5 \\  & Exp 4 & 3 & 2 & 5 & 5 & 4 \\ \hline \multirow{3}{*}{**Meta-Llama-3-8B**} & Exp 1 & 0 & 3 & 5 & 5 & 5 \\  & Exp 2 & 5 & 4 & 5 & 5 & 3.5 \\  & Exp 3 & 0 & 4 & 5 & 4 & 3.5 \\  & Exp 4 & 3 & 2 & 5 & 5 & 1 \\ \hline \multirow{3}{*}{**zephyr-7b-beta**} & Exp 1 & 3 & 5 & 5 & 5 & 5 \\  & Exp 2 & 4 & 5 & 5 & 5 & 5 \\  & Exp 3 & 0 & 4 & 5 & 4 & 4 \\  & Exp 4 & 1 & 2 & 5 & 5 & 5 \\ \hline \multirow{3}{*}{**orca\_mini\_v7\_7b**} & Exp 1 & 5 & 5 & 5 & 4 & 5 \\  & Exp 2 & 5 & 5 & 5 & 4 & 4 \\ \cline{1-1}  & Exp 3 & 5 & 5 & 5 & 4 & 4 \\ \cline{1-1}  & Exp 4 & 4 & 2 & 5 & 5 & 3 \\ \hline \multirow{3}{*}{**GPT-4**} & Exp 1 & 5 & 5 & 5 & 5 & 4 \\ \cline{1-1}  & Exp 2 & 5 & 5 & 5 & 4 & 4 \\ \cline{1-1}  & Exp 3 & 5 & 5 & 5 & 5 & 3.5 \\ \cline{1-1}  & Exp 4 & 2 & 2 & 5 & 5 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation scores of answers in each experiment. Figure 7: Calculation of the model utility score. performance of the open-source models suggested that they were well-suited for handling structured legal text generation tasks. Latency varied across the models, with GPT-4 and orca_mini_v7_7b generating responses faster than other open-source options, which could be advantageous in practical applications requiring timely outputs. For small and medium enterprises handling time-sensitive compliance requests, this latency difference could be a deciding factor. Future studies could investigate latency optimization to improve the responsiveness of open-source models for such scenarios. GPT-4 showed lower latency, which is very beneficial when real-time interaction is required, for example, answering Data Subject Access Requests within the 30-day deadline under the GDPR. However, latency for Qwen2-7b remained acceptable for batch processing tasks like periodic audits of processing activities. These results confirmed that open-source models had strong baseline potential. For example, the structured outputs and contextual accuracy of Qwen2-7B made it well suited for automating records of processing activities, where detailed documentation is required under GDPR Article 30. By reducing errors in categorization, these models can reduce the workload for compliance officers, particularly in repetitive documentation tasks. **Experiment 2--performance on model-specific prompt**. In Experiment 2, we tested whether model-specific prompts aligned with the pretraining format could improve model performance when generating GDPR documentation.