Towards Enhanced RAC Accessibility: Leveraging Datasets and LLMs
<|reference_start|>Towards Enhanced RAC Accessibility: Leveraging Datasets and LLMs: 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.<|reference_end|>
@article{sepulveda2024towards,
    title={Towards Enhanced RAC Accessibility: Leveraging Datasets and LLMs},
    author={Edison Jair Bejarano Sepulveda, Nicolai Potes Hector, Santiago Pineda
  Montoya, Felipe Ivan Rodriguez, Jaime Enrique Orduy, Alec Rosales Cabezas,
  Danny Traslavi~na Navarrete, Sergio Madrid Farfan},
    journal={arXiv preprint arXiv:2405.08792},
    year={2024},
    number={ID:28},
    archivePrefix={arXiv},
    eprint={2405.08792},
    primaryClass={cs.LG cs.AI}
}