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Can Large Language Models Explain Flight Safety Eve... | AI Research

Key Takeaways

  • Can Large Language Models Explain Flight Safety Events?
  • A Prior-Guided Semantic LLM-based Approach introduces FlightLLM, a framework designed to interpret th...
  • 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.
Paper AbstractExpand

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.

Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach introduces FlightLLM, a framework designed to interpret the causes of flight safety incidents by combining numerical flight data with the reasoning capabilities of Large Language Models (LLMs). The researchers aim to bridge the gap between traditional "black-box" machine learning models—which can detect risks but struggle to explain them—and the need for clear, actionable insights into pilot behavior and operational factors.

Addressing the Interpretation Gap

Traditional AI methods for analyzing flight data, such as those using SHAP or attention mechanisms, often produce outputs that require significant translation by domain experts to be understood. FlightLLM attempts to solve this by using LLMs to generate natural language explanations for flight safety events. The authors identify four primary challenges in this process: the difficulty of feeding complex time-series data into language models, the limited classification accuracy of LLMs on numerical data, the scarcity of labeled safety event data for training, and the risk of the model generating inaccurate information (hallucinations) due to a lack of aviation-specific knowledge.

The FlightLLM Architecture

To process flight data, the authors developed a multi-stage pipeline:

  • Feature Engineering: The system uses a dual-branch approach, combining automated statistical descriptors (extracted via the TSFresh library) with manually designed physical indicators based on aviation domain knowledge.

  • Semantic Discretization: This module converts abstract numerical patterns into qualitative descriptions, making the data compatible with the linguistic reasoning of LLMs.

  • Statistical Expert Hinting: Because LLMs are not inherently strong classifiers, the researchers integrated a CatBoost model to act as a "statistical expert." The predictions from this model are injected into the LLM’s prompt as prior guidance.

  • Contrastive Few-Shot Learning: To address data scarcity, the system retrieves similar normal and risk-event samples to provide the LLM with a contrastive context, helping it distinguish between safe and unsafe flight patterns without requiring extensive fine-tuning.

  • Structured Prompting: The researchers designed specific prompts to embed aviation knowledge, which serves to guide the LLM’s reasoning and reduce the likelihood of incorrect explanations.

Performance and Results

The authors evaluated FlightLLM using a dataset of 704 real-world A320 flight samples, focusing on "hard landing" as a representative risk event. According to the study, the approach achieves competitive classification performance while providing direct, reasonable textual explanations for the causes of these events. The researchers state that this method allows for a detailed causal diagnosis, which can be used to support pilot training and flight operation quality assurance.

Limitations and Considerations

The authors note that while their approach improves interpretability, it relies on the quality of the initial feature engineering and the accuracy of the statistical expert (CatBoost). The study acknowledges that QAR data is highly complex, containing high-dimensional multivariate time series that are inherently difficult for LLMs to process directly. Consequently, the system depends on the effectiveness of the Semantic Discretization module to bridge the gap between numerical sensor data and natural language. The researchers emphasize that this work is intended to provide a reliable reference for risk analysis, though it remains a data-driven approach subject to the limitations of the input data and the domain knowledge embedded in the prompts.

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