# LLMs in the Real World: Evaluating "AI" in Emergency Contexts

arXiv: https://arxiv.org/abs/2607.00019 · HTML: https://mlsanity.com/paper/2607.00019/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 80 references that are in the map.
- First posted on arXiv 29 May 2026.

## Builds on (most cited first, 20 of 20)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://mlsanity.com/paper/2210.07700/) (2022)
- [Retrieval-Augmented Generation for Natural Language Processing: A Survey](https://mlsanity.com/paper/2407.13193/) (2024)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research](https://mlsanity.com/paper/2305.02797/) (2023)
- [SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes](https://mlsanity.com/paper/2403.07726/) (2024)
- [Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?](https://mlsanity.com/paper/2409.19151/) (2024)
- [Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors](https://mlsanity.com/paper/2310.16924/) (2023)
- [Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem](https://mlsanity.com/paper/2406.15625/) (2024)
- [Investigating the Shortcomings of LLMs in Step-by-Step Legal Reasoning](https://mlsanity.com/paper/2502.05675/) (2025)
- [Estimating post-editing effort: a study on human judgements, task-based and reference-based metrics of MT quality](https://mlsanity.com/paper/1910.06204/) (2019)
- [Data Laundering: Artificially Boosting Benchmark Results through Knowledge Distillation](https://mlsanity.com/paper/2412.15255/) (2024)
- [Leveraging Domain Knowledge at Inference Time for LLM Translation: Retrieval versus Generation](https://mlsanity.com/paper/2503.05010/) (2025)
- [You Cannot Feed Two Birds with One Score: the Accuracy-Naturalness Tradeoff in Translation](https://mlsanity.com/paper/2503.24013/) (2025)
- [Feeding Two Birds or Favoring One? Adequacy-Fluency Tradeoffs in Evaluation and Meta-Evaluation of Machine Translation](https://mlsanity.com/paper/2509.20287/) (2025)
- [No Size Fits All: The Perils and Pitfalls of Leveraging LLMs Vary with Company Size](https://mlsanity.com/paper/2408.01444/) (2024)
- [Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statements](https://mlsanity.com/paper/2411.07845/) (2024)

## Abstract (arXiv)

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.