大语言模型能否解释飞行安全事件？一种先验引导的基于语义大语言模型的方法
Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach
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中文总结 AI 辅助
本研究针对飞行安全事件解释难题，提出FlightLLM方法，结合特征工程、语义离散化、CatBoost先验引导及对比小样本学习等技术，在704个A320飞行样本上实现了良好分类与合理事件原因解释。
中文摘要 AI 辅助
利用飞行数据提升飞行安全，不仅需要准确检测风险事件，更重要的是在飞行员控制行为层面清晰解释其根本原因。现有可解释人工智能技术（如特征重要性图）往往需要大量领域知识才能将其转化为具有操作意义的解释。擅长语言推理的大语言模型（LLMs）为该问题带来了有前景的解决方案，但将LLMs应用于该领域存在模态不一致、分类能力有限、微调所需的任务特定数据稀缺以及缺乏领域知识等关键挑战。为克服这些挑战，我们提出FlightLLM，一种先验引导的基于语义大语言模型的可解释飞行安全分析方法。具体而言，我们首先进行特征工程以解决模态不一致问题，将统计描述符与具有物理意义的飞行指标相结合。该表示进一步由语义离散化模块处理，该模块将抽象数值模式转换为更适配语言推理的定性描述。此外，由于LLMs并非天生的强分类器，我们引入CatBoost作为统计专家，并将其预测结果作为先验引导注入提示中。我们还采用对比小样本学习策略以弥补数据有限的问题。最后，我们设计结构化提示将航空特定知识嵌入推理过程。我们以硬着陆（一种具有复杂因果机制的代表性风险事件）为锚点，在包含704个真实A320飞行样本的数据集上评估FlightLLM。实验结果表明，所提出的方法在实现有竞争力的分类性能的同时，能生成直接且合理的事件原因解释。
英文摘要
Improving flight safety with flight data requires not only accurate detection of risk events, but more importantly, clear interpretation of their underlying causes at the level of pilot control behavior. Existing explainable AI techniques, such as feature importance maps, often require considerable domain knowledge to translate them into operationally meaningful explanations. Large Language Models (LLMs), which excel at language reasoning, bring a promising solution to this issue. However, applying LLMs in this domain presents key challenges such as modal inconsistency, limited classification ability, scarcity of task-specific data for fine-tuning, and lack of domain knowledge. To overcome these challenges, we propose FlightLLM, a prior-guided semantic LLM-based approach for interpretable flight safety analysis. Specifically, we first perform feature engineering to address modal inconsistency, combining statistical descriptors with physically meaningful flight indicators. This representation is further processed by a Semantic Discretization module, which converts abstract numerical patterns into qualitative descriptions that are more compatible with language reasoning. In addition, since LLMs are not inherently strong classifiers, CatBoost is incorporated as a statistical expert, and its prediction results are injected into the prompt as prior guidance. A contrastive few-shot learning strategy is further adopted to compensate for limited data. Finally, we design structured prompts to embed aviation-specific knowledge into the inference process. Using hard landing, a representative risk event with complex causal mechanisms, as an anchor point, we evaluate FlightLLM on a dataset of 704 real-world A320 flight samples. Experimental results show that the proposed approach achieves competitive classification performance while generating direct and reasonable explanations for event causes.
发表机构
- College of Computer Science, Chongqing University（重庆大学计算机学院）
- Sichuan Flight Engineering Technology Research Center, Civil Aviation Flight University of China（中国民航飞行学院四川飞行工程技术研究中心）
- School of Statistics and Data Science, Shanghai University of International Business and Economics（上海对外经贸大学统计与数据科学学院）
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