This paper explores the potential of large language models (LLMs) to make the Aeronautical Regulations of Colombia (RAC) more accessible. Given the complexity and extensive technicality of the RAC, this study introduces a novel approach to simplifying these regulations for broader understanding. By developing the first-ever RAC database, which contains 24,478 expertly labeled question-and-answer pairs, and fine-tuning LLMs specifically for RAC applications, the paper outlines the methodology for dataset assembly, expert-led annotation, and model training. Utilizing the Gemma1.1 2b model along with advanced techniques like Unsloth for efficient VRAM usage and flash attention mechanisms, the research aims to expedite training processes. This initiative establishes a foundation to enhance the comprehensibility and accessibility of RAC, potentially benefiting novices and reducing dependence on expert consultations for navigating the aviation industry's regulatory landscape. You can visit the dataset (https://huggingface.co/somosnlp/gemma-1.1-2b-it_ColombiaRAC_FullyCurated_format_chatML_V1) and the model (https://huggingface.co/datasets/somosnlp/ColombiaRAC_FullyCurated) here.

翻译：本文探讨了大语言模型（LLMs）在提升《哥伦比亚航空条例》（RAC）可访问性方面的潜力。针对RAC内容复杂且专业术语繁多的特点，本研究提出了一种创新方法以简化该条例，促进其广泛理解。通过构建首个包含24,478个经专家标注的问答对的RAC数据库，并针对RAC应用场景对LLMs进行微调，本文详细阐述了数据集构建、专家主导标注及模型训练的方法论。研究采用Gemma1.1 2b模型，结合Unsloth高效显存利用技术及闪存注意力机制等先进技术，旨在加速训练进程。该举措为提升RAC的可理解性与可访问性奠定了基础，有望惠及初学者，并减少航空业监管领域对专家咨询的依赖。数据集（https://huggingface.co/somosnlp/gemma-1.1-2b-it_ColombiaRAC_FullyCurated_format_chatML_V1）及模型（https://huggingface.co/datasets/somosnlp/ColombiaRAC_FullyCurated）可在此处访问。