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
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- PossiblePossibly related (embedding) · 53%Agentic AI and cybersecurity, the story so far →
- FuzzySimilar title/name (fuzzy) · 59%microsoft/semantic-kernel →
“Fuzzy title match (0.73): “Can Large Language Models Explain Flight Safety Events? A Pr” ≈ “microsoft/semantic-kernel””
- LinkedLinked via arxiv author · 85%Lu Xu →
“Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach”
- LinkedLinked via arxiv author · 85%Xu Lin →
“Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach”
- LinkedLinked via arxiv author · 85%Linjiang Zheng →
“Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach”
- LinkedLinked via arxiv author · 85%Xiaofan Lin →
“Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach”
- LinkedLinked via arxiv author · 85%Riquan Zhang →
“Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach”
- LinkedLinked via arxiv author · 85%Jiaxing Shang →
“Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach”