Air Traffic Control Using Large Language Models: Prompt Engineering, Architecture, and Evaluation
Researchers have explored the use of large language models to generate air traffic control communications. They experimented with nine different models and found that providing a worked example can improve similarity between generated and actual conversations. However, overly scripted prompts can lead to errors accumulating through dialogue. The study suggests that using light prompts and injecting correct history into the conversation can be beneficial for LLM-assisted air t
Researchers have explored the use of large language models to generate air traffic control communications. They experimented with nine different models and found that providing a worked example can improve similarity between generated and actual conversations. However, overly scripted prompts can lead to errors accumulating through dialogue. The study suggests that using light prompts and injecting correct history into the conversation can be beneficial for LLM-assisted air traffic control.
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Why it matters: This research is important for engineers working on AI-powered air traffic control systems because it provides insights into how to design effective language models for this critical application. Understanding how to balance prompt structure with error accumulation can help improve the safety and efficiency of air traffic management.
Source: https://arxiv.org/abs/2608.19299
This article was originally published at: https://arxiv.org/abs/2608.19299