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LLMs tested for air traffic control communications

Researchers have explored the use of large language models (LLMs) for air traffic control (ATC) communications, a critical dialogue system. They conducted experiments using a simulated general-aviation flight over San Francisco, designing five prompt structures to evaluate LLM performance. The study found that providing an example transcript improved similarity scores, while overly constrained prompts led to error accumulation. Injecting correct history into the dialogue pipeline helped repair these errors, outlining current limitations and a path toward LLM-assisted ATC. AI

IMPACT This research suggests a potential future for LLMs in safety-critical communication systems, though current limitations need to be addressed.

RANK_REASON The cluster contains an academic paper detailing research on applying LLMs to a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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LLMs tested for air traffic control communications

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mahyar Ghazanfari, Matthias Casanova, Jordan Kam, Alex Zongo, Peng Wei, Torsten Darrell, Alexandre Bayen ·

    Air Traffic Control Using Large Language Models: Prompt Engineering, Architecture, and Evaluation

    arXiv:2608.19299v1 Announce Type: new Abstract: Air traffic control (ATC) communication is a safety-critical dialogue that remains largely human-driven even as other parts of air traffic management have been semi-automated. In this article, we experimentally evaluate whether larg…