Summary of Towards Enhanced Rac Accessibility: Leveraging Datasets and Llms, by Edison Jair Bejarano Sepulveda et al.
Towards Enhanced RAC Accessibility: Leveraging Datasets and LLMs
by Edison Jair Bejarano Sepulveda, Nicolai Potes Hector, Santiago Pineda Montoya, Felipe Ivan Rodriguez, Jaime Enrique Orduy, Alec Rosales Cabezas, Danny Traslaviña Navarrete, Sergio Madrid Farfan
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the application of large language models (LLMs) in making the Aeronautical Regulations of Colombia (RAC) more accessible. The researchers introduce a novel approach to simplify these regulations by developing an RAC database containing expertly labeled question-and-answer pairs and fine-tuning LLMs for RAC applications. The study outlines the methodology for dataset assembly, annotation, and model training using techniques such as Unsloth for efficient VRAM usage and flash attention mechanisms. This initiative aims to enhance the comprehensibility and accessibility of RAC, potentially benefiting novices and reducing dependence on expert consultations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how big language models can help make important aviation rules more understandable. The researchers create a special database with 24,478 questions and answers about these rules, which are very complex. They then train a model using this data to simplify the rules. This can be helpful for people who aren’t experts in aviation and need to understand the rules. |
Keywords
» Artificial intelligence » Attention » Fine tuning
