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.

翻译：ChatGPT和GPT-4的出现在大型语言模型（LLMs）领域引起了全球关注，其在问答、摘要和内容生成方面展现了卓越性能。航空行业的特点是存在大量复杂、非结构化的文本数据，其中充斥着技术行话和专业术语。此外，该领域的模型构建标注数据十分稀缺，导致航空文本数据利用率低下。大型语言模型的出现为改变这一现状提供了契机，但目前缺乏专门针对航空领域设计的大型语言模型。为填补这一空白，我们提出航空GPT，它基于开源LLaMA-2和Mistral架构构建，并在大量精心整理的航空数据集上持续训练。实验结果表明，航空GPT为用户提供多种优势，包括应对多种自然语言处理（NLP）问题的灵活性（例如问答、摘要、文档撰写、信息抽取、报告查询、数据清洗和交互式数据探索）。它还能在航空领域内提供准确且上下文相关的响应，并显著提升性能（例如，在测试案例中性能提升超过40%）。借助航空GPT，航空行业能够更好地解决更复杂的研究问题，并提升国家空域系统（NAS）运行的效率和安全性。