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        <title>LLMs in the Real World: Evaluating “AI” in Emergency Contexts</title>
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            <namePart type="given">Viviane</namePart>
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            <namePart type="given">Jiajun</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T LLMs in the Real World: Evaluating “AI” in Emergency Contexts
%A Court, Sara
%A Downing, Lara
%A Elsner, Micha
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F court-etal-2026-llms
%X 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.
%U https://aclanthology.org/2026.findings-acl.1779/
%P 35748-35760

Markdown (Informal)

[LLMs in the Real World: Evaluating “AI” in Emergency Contexts](https://aclanthology.org/2026.findings-acl.1779/) (Court et al., Findings 2026)

• LLMs in the Real World: Evaluating “AI” in Emergency Contexts (Court et al., Findings 2026)

ACL

• Sara Court, Lara Downing, and Micha Elsner. 2026. LLMs in the Real World: Evaluating “AI” in Emergency Contexts. In Findings of the Association for Computational Linguistics: ACL 2026, pages 35748–35760, San Diego, California, United States. Association for Computational Linguistics.