## 4 Experiments and Results
### Utility Evaluation  
In the following, we combined the results of the experiments and the benchmarks as defined in Section 3.5 to derive a final utility score for each model concerning the use case. The utility scores balanced the results of the experiments and benchmarks and reflected accuracy, contextual understanding, and adherence to formatting requirements. The scores are listed in Figure 9 and Table 6.  
The utility scores revealed that the open-source Qwen2-7B model led the performance rankings, even outperforming GPT-4. Qwen2-7B excelled in out-of-the-box performance and demonstrated a good contextual understanding throughout Experiments 1-3. This highlights its strength in tasks requiring immediate domain adaptation and structured response generation, offering organizations a cost-effective alternative for GDPR compliance tasks without sacrificing accuracy. GPT-4 continued to show a robust performance across the benchmarks. In particular, it excelled in the multiple-choice benchmark, highlighting its advanced understanding of the specifics of the GDPR. The high scores of GPT-4 in contextual understanding across the experiments ensured that edge cases, such as ambiguous data categories, were handled with greater accuracy, which reduced the risk of misclassification. Qwen2-7B proved that open-source models were a viable choice for GDPR compliance tasks. While GPT-4 exhibited lower latency, the 7-10 s delay in generating outputs from Qwen2-7B remained acceptable for batch processing tasks, ensuring that organizations could meet GDPR documentation timelines without significant interruptions. Furthermore, the capability of Qwen2-7B to generate structured lists with 90% accuracy can be directly applied to creating other GDPR-mandated records, such as data subject access requests. This would reduce the workload of compliance officers by automating repetitive tasks, allowing them to focus on legal review. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Correct Answer Rate (\%)** & **Score** \\ \hline
**Qwen2-7B** & 44.0\% & 3 \\
**Meta-Llama-3-8B** & 40.4\% & 3 \\
**zephyr-7b-beta** & 34.8\% & 1 \\
**orca\_mini\_v7\_7b** & 34.8\% & 1 \\
**GPT-4** & 43.2\% & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Scores per model for the generation benchmark. Among the other open-source models, orca_mini_v7_7b showed consistency in generation tasks, performing well in Experiments 1 and 2. Orca demonstrated strong knowledge retention, as reflected in its performance on the multiple-choice benchmark. However, its lower performance in the generation benchmark suggests limitations in handling unstructured tasks, which may reflect the need for more advanced prompt engineering. However, orca_mini_v7_7b is more resource-efficient, making it suitable for smaller organizations seeking compliance solutions on a budget. Conversely, Meta-Llama-3-8B was the only model to show adaptability in Experiment 4, suggesting an ability to handle novel contexts. Nevertheless, it struggled to maintain consistency across structured GDPR tasks, as evidenced by its moderate benchmark scores. In addition, the lower contextual understanding of Meta-Llama-3-8B could result in incomplete compliance documentation, requiring additional human review. This highlights the need for fine-tuning models on legal-specific datasets. Zephyr-7b-beta ranked lowest in overall utility. Nevertheless, it achieved the highest score in Experiment 2, demonstrating its adaptation to model-specific prompts. This suggests potential for fine-tuning and task-specific optimization, but also highlights weaknesses in generalization, as evidenced by its low performance on the generation benchmark. The performance metrics of Qwen2-7B and GPT-4 demonstrate that LLMs are capable of automating critical GDPR documentation tasks, such as generating records of processing activities and data categorization.