  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.
