Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach
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 overcom...
| Authors | Lu Xu, Xu Li, Linjiang Zheng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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