## 3 Methodology
### LLM Selection
Based on these five criteria, we identified a set of LLMs that balanced performance, accessibility, and compliance with GDPR-specific requirements. The selected models included both state-of-the-art open-source options and a closed-source baseline to provide a comprehensive comparison. Table 1 summarizes the key characteristics of these models, highlighting their suitability for GDPR documentation tasks. * Qwen2-7B [31], released by Alibaba, was chosen due to its strong performance in multilingual contexts, as it was trained in 27 languages. As GDPR compliance often involves handling data in multiple countries within the EU with different languages, Qwen2-7B is well suited for applications in different linguistic environments. With an average score of 42.91 on the leaderboard, the model ranked among the top performing open-source LLMs as of July 2024, making it a reliable choice for GDPR documentation tasks. * Meta-Llama-3-8B [32] was released in April 2024 by Meta and is trained on an offline dataset. This model is optimized for interactive, instruction-driven use cases, which aligns well with the requirements of GDPR documentation, where precise and structured responses are required. This ability is underlined by high performance on benchmarks such as MUSR and GPQA, which test reasoning and problem-solving skills. This suggested that it could effectively deal with the structured complexity of the GDPR categories. * zephyr-7b-beta [33] was created by the H4 Team of HuggingFace, and introduced on 25 October 2023. zephyr-7b-beta is based on the popular Mistral-7B model and was tuned with UltraChat [34] and UltraFeedback [33] datasets to improve the quality of instruction-following. The high benchmark performance, particularly in MMLU-PRO and IFEval, demonstrates that zephyr-7b-beta effectively handles complex promptsand instruction-following tasks, making it a strong candidate for GDPR documentation generation. * [leftmargin=*] orca_mini_v7_7b [35] was created by [PERSON], one of the leading LLM engineers on HuggingFace. The model was released in May 2024 and is a fine-tuned version of the Qwen2 model. This model was chosen for its approach to overcoming size limitations through imitation learning techniques that mimic the reasoning processes found in larger models [36]. Fine-tuning on orca-style datasets [36] makes it particularly adept at handling nuanced regulatory information and legal reasoning tasks. * [leftmargin=*] GPT-4 [20] released by OpenAI in March 2023, excels at complex tasks, outperforming all state-of-the-art systems on traditional NLP benchmarks, making it the most capable model as of July 2024. It is also known to excel at complex reasoning and nuanced instruction-following tasks. GPT-4 sets a high standard for handling complex prompts and language, making it an ideal baseline against which to compare open-source models. Other high scoring open-source models from the leaderboard were considered in our selection. The Mistral model was excluded because the Zephyr-7B-beta model, which is based on Mistral, scored higher on the leaderboard in July 2024. While zephyr-7B-beta incorporates additional fine-tuning with datasets such as UltraChat [34] and UltraFeedback [33], its selection was based on its strong performance as a pretrained and instruction-tuned model. In contrast, other fine-tuned models on the leaderboard were excluded due to nontransparent training processes and datasets. Many of the top performing models were fine-tuned by community members without detailed documentation of the datasets or methodologies used. This lack of transparency could introduce unknown biases or limitations, making these models less suitable for the goal of our study. By prioritizing models with well-documented pretraining and instruction-tuning processes, we ensured a neutral assessment of foundational performance while providing a robust baseline for future fine-tuning in GDPR-specific contexts. The next step involved applying the selected models within the targeted use case.