AviationGPT：面向航空领域的大语言模型
AviationGPT: A Large Language Model for the Aviation Domain
AI总结：
本文提出基于LLaMA-2和Mistral架构、在航空数据集上持续训练的AviationGPT，以解决航空领域专用大语言模型缺失问题，并在多种NLP任务中实现超过40%的性能提升。
摘要：
ChatGPT和GPT-4的问世让大语言模型（LLMs）引起全球关注，它们在问答、摘要和内容生成方面展现出卓越性能。航空业拥有大量复杂、非结构化的文本数据，其中充满技术行话和专业术语。此外，该领域用于模型构建的标注数据十分稀缺，导致航空文本数据的利用率很低。LLMs的出现为改变这一局面提供了机会，但目前缺乏专门为航空领域设计的LLMs。为填补这一空白，我们提出AviationGPT，它基于开源的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，AviationGPT为用户提供了多重优势，包括能够灵活处理多种自然语言处理（NLP）问题（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与上下文相关的回答，并显著提升性能（例如在测试案例中性能提升超过40%）。借助AviationGPT，航空业能够更好地应对更复杂的研究问题，并提高国家空域系统（NAS）运行的效率和安全性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
