---
title: "FlightLLM：一种先验引导的语义大语言模型方法用于飞行安全事件解释"
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","可解释AI","少样本学习","航空安全"]
---

# FlightLLM：一种先验引导的语义大语言模型方法用于飞行安全事件解释

## AI 点评

> 这篇论文最值得关注的是它摒弃了把LLM当全能分类器的思路，而是让CatBoost负责预测、LLM负责语义解释，形成一种‘统计+语言’的协作范式。这种设计对工业场景中落地大模型很有启发，尤其是那些既需要准确判断又需要可解释结论的领域。对航空数据科学家、可解释AI研究者以及安全监测系统设计者来说，这篇文章值得一读。

## 速览

这篇论文针对飞行安全事件解释中的模态不一致、大模型分类能力有限、微调数据稀缺和领域知识缺乏等挑战，提出FlightLLM框架。该方法通过特征工程融合统计描述符与物理飞行指标，利用语义离散化模块将数值模式转换为定性描述，并引入CatBoost作为统计专家注入预测先验，结合对比少样本学习与结构化提示词嵌入航空知识。在704份真实A320航班数据上以硬着陆事件为锚点评估，FlightLLM在取得有竞争力分类性能的同时，能生成直接合理的因果关系解释，为飞行安全归因提供了新范式。

## 关键点

- 提出FlightLLM框架，结合语义离散化与先验引导，解决LLM在飞行安全分析中的多模态和推理问题
- 融合CatBoost作为统计专家，将预测结果注入提示词，弥补LLM固有分类能力不足
- 采用对比少样本学习应对任务数据稀缺，并利用结构化提示词嵌入航空领域知识
- 在704个真实A320航班样本上验证了硬着陆事件分类与解释的有效性

## 全文

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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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_b965a93c4dbc49a3306e4c34