Aerospace Language Understanding Evaluation (ALUE): Large Language Benchmark with Aerospace Datasets
Eugene Mangortey, Kunal Sarkhel, Satyen Singh, Shuo Chen, Bulent Ayhan, Shreya Kurdukar · 2025
Large Language Models (LLMs) present revolutionary potential for the aviation industry, enabling stakeholders to derive critical intelligence and improve operational efficiencies through automation. However, given the safety-critical nature of aviation, a rigorous domain-specific evaluation of LLMs is paramount before their integration into workflows. General-purpose LLM benchmarks often do not capture the nuanced understanding of aerospace-specific knowledge and the phraseology required for reliable application. This paper introduces the Aerospace Language Understanding Evaluation (ALUE) benchmark, an aviation-specific framework designed for scalable evaluation, assessment, and benchmarking of LLMs against specialized aviation datasets and language tasks. ALUE incorporates diverse datasets and tasks, including binary and multiclass classification for hazard identification, extractive question answering for precise information retrieval (e.g., tail numbers, runways), sentiment analysis, and multiclass token classification for fine-grained analysis of air traffic control communications. ALUE also introduces several metrics for evaluating the correctness of generated responses utilizing LLMs to identify and judge claims made in generated responses. Our findings demonstrate that structured prompts and in-context examples significantly improve model performance, highlighting that general models struggle with aviation tasks without such guidance and often produce verbose or unstructured outputs. ALUE provides a crucial tool for guiding the development and safe deployment of LLMs tailored to the unique demands of the aviation and aerospace domains.