AviationLLM：一种基于大语言模型的航空训练知识系统
AviationLLM: An LLM-based Knowledge System for Aviation Training
AI总结：
针对航空训练中教员不足、专业知识获取准确性低的问题，提出RALA-DPO方法，结合DPO领域对齐与RAG技术，适配Qwen模型用于航空理论训练，提升问答准确率并支持零成本知识更新。
摘要：
航空训练是保障飞行安全、提升行业效率、推动可持续发展的核心环节，它不仅涉及飞行模拟，还需要学习大量专业航空理论知识。现有训练体系中，知识主要由教员传授，但教员数量有限，且从互联网获取的专业答案准确性不足，导致训练效率低下。\n为解决这一问题，我们引入了大语言模型（LLM），但基础预训练模型无法针对专业领域给出准确答案，因此我们对其进行微调。传统监督微调（SFT）因数据覆盖不足，存在生成看似合理但事实错误的回答的风险。对此，我们采用直接偏好优化（DPO）方法。\n本文提出了基于直接偏好优化的检索增强大语言模型对齐方法（RALA-DPO）。我们选取开源预训练大语言模型Qwen，通过基于DPO的领域对齐使其适配航空理论训练场景。同时，为缓解训练数据偏差、知识过时或领域知识缺口引发的幻觉问题，我们实现了检索增强生成（RAG）技术，融合生成模型与检索模型。RALA-DPO可从外部知识库中有效检索相关信息，并通过生成模型输出精准、高质量的回答。\n实验结果表明，RALA-DPO能提升航空专业知识问答的准确性。结合集成的RAG机制后，该系统可进一步提升答案准确率，同时实现零成本知识更新。
英文摘要：
Aviation training is a core link in ensuring flight safety, improving industry efficiency and promoting sustainable development. It not only involves flight simulation but also requires the learning of a great deal of professional aviation theory knowledge. In the existing training system, the knowledge is mainly imparted by the the instructors. However, the number of instructors is limited and the professional answers obtained from the Internet are not accurate enough, resulting in low training efficiency. To address this, we introduced LLM, but the basic pre-trained model cannot provide accurate answers to professional fields, so we fine-tuned it. Traditional Supervised Fine-Tuning (SFT) risk generating superficially plausible but factually incorrect responses due to insufficient data coverage. To address this, we employ Direct Preference Optimization(DPO). This paper proposes Retrieval-Augmented LLM Alignment via Direct Preference Optimization(RALA-DPO). We select open source pre-trained LLM Qwen and adapt it to aviation theory training through DPO-based domain alignment. Simultaneously, to mitigate hallucinations caused by training data biases, knowledge obsolescence, or domain knowledge gaps, we implement Retrieval-Augmented Generation(RAG) technology that combines generative and retrieval models. RALA-DPO effectively retrieves relevant information from external knowledge bases and delivers precise and high-quality responses through the generative model. Experimental results demonstrate that RALA-DPO can improve accuracy in response to professional aviation knowledge. With integrated RAG mechanisms, this system can further improve the accuracy of answers and achieve zero-cost knowledge updates simultaneously.
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。
