---
title: "FlightLLM: A Prior-Guided Semantic LLM-based Approach for Explaining Flight Safety Events"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.18017v1"
permalink: "https://feed.astralor.com/items/itm_b965a93c4dbc49a3306e4c34"
published: "2026-08-18T17:11:40.000Z"
score: 77
channel: "ai"
tags: ["CatBoost","FlightLLM","Explainable AI","Few-Shot Learning","Aviation Safety"]
---

# FlightLLM: A Prior-Guided Semantic LLM-based Approach for Explaining Flight Safety Events

## AI commentary

> The most notable aspect is that this paper avoids treating LLMs as all-in-one classifiers, instead using CatBoost for prediction and LLMs for semantic explanation, creating a 'statistics plus language' collaboration paradigm. This design offers valuable inspiration for deploying LLMs in industrial settings, especially where accurate judgment and interpretable conclusions are both essential. It is worth reading for aviation data scientists, explainable AI researchers, and safety monitoring system designers.

## At a glance

This paper proposes FlightLLM, a prior-guided semantic LLM-based approach for interpretable flight safety analysis. It addresses challenges including modal inconsistency, limited classification ability of LLMs, scarcity of fine-tuning data, and lack of domain knowledge. The method performs feature engineering by combining statistical descriptors with physically meaningful flight indicators, converts abstract numerical patterns into qualitative descriptions via a Semantic Discretization module, incorporates CatBoost as a statistical expert whose predictions are injected into prompts as prior guidance, and adopts contrastive few-shot learning to compensate for limited data. Structured prompts embed aviation-specific knowledge into inference. Evaluated on a dataset of 704 real-world A320 flight samples focusing on hard landing events, FlightLLM achieves competitive classification performance while generating direct and reasonable explanations for event causes. This demonstrates a promising new paradigm for flight safety event attribution.

## Key points

- Proposes FlightLLM, combining semantic discretization and prior guidance to address modal inconsistency and reasoning challenges in flight safety analysis
- Integrates CatBoost as a statistical expert, injecting predictions into prompts to compensate for LLMs' weak classification ability
- Adopts contrastive few-shot learning to handle scarce task data and uses structured prompts to embed aviation domain knowledge
- Validates the approach on 704 real-world A320 flight samples for hard landing classification and explanation

## Full text

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.

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