AviationGPT: A Large Language Model for the Aviation Domain
This addresses the problem of low usage of aviation text data due to technical jargon and scarce labeled data, enabling more efficient and safe operations in the aviation industry, though it is incremental as it adapts existing models to a new domain.
The authors tackled the lack of large language models for the aviation domain by proposing AviationGPT, built on LLaMA-2 and Mistral architectures and trained on curated aviation datasets, resulting in over a 40% performance gain in tested cases and versatile NLP capabilities.
The advent of ChatGPT and GPT-4 has captivated the world with large language models (LLMs), demonstrating exceptional performance in question-answering, summarization, and content generation. The aviation industry is characterized by an abundance of complex, unstructured text data, replete with technical jargon and specialized terminology. Moreover, labeled data for model building are scarce in this domain, resulting in low usage of aviation text data. The emergence of LLMs presents an opportunity to transform this situation, but there is a lack of LLMs specifically designed for the aviation domain. To address this gap, we propose AviationGPT, which is built on open-source LLaMA-2 and Mistral architectures and continuously trained on a wealth of carefully curated aviation datasets. Experimental results reveal that AviationGPT offers users multiple advantages, including the versatility to tackle diverse natural language processing (NLP) problems (e.g., question-answering, summarization, document writing, information extraction, report querying, data cleaning, and interactive data exploration). It also provides accurate and contextually relevant responses within the aviation domain and significantly improves performance (e.g., over a 40% performance gain in tested cases). With AviationGPT, the aviation industry is better equipped to address more complex research problems and enhance the efficiency and safety of National Airspace System (NAS) operations.