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文章背景与核心概要

提高飞行安全性不仅需要检测风险事件，还需要从飞行员的操控行为角度清晰地解释其深层原因。虽然大语言模型（LLM）具备强大的语言推理能力，但将其直接应用於航空数据时，仍面临模态不一致、原生分类能力弱、数据稀缺以及缺乏领域知识等挑战。

为了弥补这一差距，作者提出了 FlightLLM，这是一个专为可解释飞行安全分析而设计的先验引导语义大模型框架。该方法在包含 704 个聚焦於硬着陆的 A320 真实飞行样本数据集上进行了测试，结果表明其不仅具备极具竞争力的分类准确率，还能成功生成直观且具有实际操作意义的解释。

Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach

Authors: Lu Xu, Xu Li, Linjiang Zheng, Fan Li, Riquan Zhang, Jiaxing Shang
Submitted: August 18, 2026 (Submitted to IEEE Transactions on Intelligent Transportation Systems)
ArXiv ID: arXiv:2608.18017 [cs.AI]

Authors: Lu Xu, Xu Li, Linjiang Zheng, Fan Li, Riquan Zhang, Jiaxing Shang
Submitted: August 18, 2026 (Submitted to IEEE Transactions on Intelligent Transportation Systems)
ArXiv ID: arXiv:2608.18017 [cs.AI]

Summary

Improving flight safety requires going beyond the mere detection of risk events to clearly interpreting their underlying causes in terms of pilot control behavior. While Large Language Models (LLMs) offer strong language reasoning capabilities, applying them directly to aviation data presents challenges such as modal inconsistency, weak native classification abilities, data scarcity, and a lack of domain knowledge.

To bridge this gap, the authors propose FlightLLM, a prior-guided, semantic LLM-based framework designed for interpretable flight safety analysis. Tested on a real-world dataset of 704 A320 flight samples focusing on hard landings, the approach demonstrates competitive classification accuracy while successfully generating intuitive, operationally meaningful explanations.

Improving flight safety requires going beyond the mere detection of risk events to clearly interpreting their underlying causes in terms of pilot control behavior. While Large Language Models (LLMs) offer strong language reasoning capabilities, applying them directly to aviation data presents challenges such as modal inconsistency, weak native classification abilities, data scarcity, and a lack of domain knowledge.

To bridge this gap, the authors propose FlightLLM, a prior-guided, semantic LLM-based framework designed for interpretable flight safety analysis. Tested on a real-world dataset of 704 A320 flight samples focusing on hard landings, the approach demonstrates competitive classification accuracy while successfully generating intuitive, operationally meaningful explanations.

Key Methodology Components

  1. Feature Engineering & Modal Inconsistency: Combines statistical descriptors with physically meaningful flight indicators to bridge the gap between raw flight telemetry and text-based reasoning.
  2. Semantic Discretization Module: Translates abstract numerical patterns into qualitative descriptions compatible with natural language models.
  3. Statistical Expert Guidance (CatBoost): Compensates for LLMs' limitations as native classifiers by incorporating CatBoost predictions as prior guidance within the prompt.
  4. Contrastive Few-Shot Learning: Addresses task-specific data scarcity through a targeted learning strategy.
  5. Aviation-Specific Prompting: Embeds structured domain knowledge directly into the LLM inference process.

Key Methodology Components

  1. Feature Engineering & Modal Inconsistency: Combines statistical descriptors with physically meaningful flight indicators to bridge the gap between raw flight telemetry and text-based reasoning.
  2. Semantic Discretization Module: Translates abstract numerical patterns into qualitative descriptions compatible with natural language models.
  3. Statistical Expert Guidance (CatBoost): Compensates for LLMs' limitations as native classifiers by incorporating CatBoost predictions as prior guidance within the prompt.
  4. Contrastive Few-Shot Learning: Addresses task-specific data scarcity through a targeted learning strategy.
  5. Aviation-Specific Prompting: Embeds structured domain knowledge directly into the LLM inference process.

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