@inproceedings{court-etal-2026-llms,
    title = "{LLM}s in the Real World: Evaluating ``{AI}'' in Emergency Contexts",
    author = "Court, Sara  and
      Downing, Lara  and
      Elsner, Micha",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.findings-acl.1779/",
    pages = "35748--35760",
    ISBN = "979-8-89176-395-1",
    abstract = "This paper offers a call to action. We urge our colleagues in the research community to play a greater role in the articulation of our findings to the public. To illustrate the stakes we present a case study on the initial stages of an LLM-based machine translation application{'}s deployment in a real-world context: a text-2-911 system advertising capabilities in 55 languages for use in emergencies in which it may be difficult to call operators directly. We identify a number of common misconceptions about technologies such as these, concluding with a set of concrete recommendations and best practices for stakeholders at every stage of the development and deployment pipeline. While the advancement of scientific research often lies in solving the ``hard'' problems, we argue it is often the ``easy'' ones{---} problems for which the latest technology is often unnecessary{---} that are most overlooked."
}