NOTAM-Evolve：基于LLMs的知识引导自进化优化框架，用于NOTAM解读
NOTAM-Evolve: A Knowledge-Guided Self-Evolving Optimization Framework with LLMs for NOTAM Interpretation
AI总结：
针对NOTAM解读的挑战，提出NOTAM-Evolve框架，结合LLMs、知识图谱和闭环学习实现动态知识锚定与模式推理，在新基准数据集上准确率较基础LLM提升30.4%，推动NOTAM深度解析的自动化水平。
摘要：
准确解读航行通告（NOTAMs）对航空安全至关重要，但其简洁隐晦的语言给人工和自动化处理带来巨大挑战。现有自动化系统通常局限于浅层解析，无法提取操作决策所需的可执行情报。我们将完整解读任务形式化为深度解析，这是一项双重推理挑战，需动态知识锚定（将NOTAM与动态现实航空数据关联）和基于模式的推理（应用静态领域规则推导操作状态）。为应对该挑战，我们提出NOTAM-Evolve，一种知识引导的自进化框架，利用大语言模型（LLMs）实现NOTAM解读。该框架通过知识图谱增强的检索模块实现动态知识锚定，并结合静态领域规则库进行基于模式的推理，同时引入闭环学习过程，使LLM从自身输出中持续进化，减少对大规模人工标注推理轨迹的依赖。我们构建了包含10,000条专家标注NOTAM的基准数据集，并进行实验验证。结果表明，NOTAM-Evolve在结构化NOTAM解读任务上的准确率较基础LLM提升30.4%，达到当前最佳水平。
英文摘要：
Accurate interpretation of Notices to Airmen (NOTAMs) is critical for aviation safety, yet their condensed and cryptic language poses significant challenges to both manual and automated processing. Existing automated systems are typically limited to shallow parsing, failing to extract the actionable intelligence needed for operational decisions. We formalize the complete interpretation task as deep parsing, a dual-reasoning challenge requiring both dynamic knowledge grounding (linking the NOTAM to evolving real-world aeronautical data) and schema-based inference (applying static domain rules to deduce operational status). To tackle this challenge, we propose NOTAM-Evolve, a self-evolving framework that enables a large language model (LLM) to autonomously master complex NOTAM interpretation. Leveraging a knowledge graph-enhanced retrieval module for data grounding, the framework introduces a closed-loop learning process where the LLM progressively improves from its own outputs, minimizing the need for extensive human-annotated reasoning traces. In conjunction with this framework, we introduce a new benchmark dataset of 10,000 expert-annotated NOTAMs. Our experiments demonstrate that NOTAM-Evolve achieves a 30.4% absolute accuracy improvement over the base LLM, establishing a new state of the art on the task of structured NOTAM interpretation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
