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Sustaining Human Agency, Attending to Its Cost: An Investigation into Generative AI Design for Non-Native Speakers' Language Use

https://doi.org/10.1145/3706598.3713626

Xiao, Yimin; Hancock, Cartor; Agrawal, Sweta; Mehandru, Nikita; Salehi, Niloufar; Carpuat, Marine; Gao, Ge (April 2025, ACM)

Free, publicly-accessible full text available April 25, 2026
Sustaining Human Agency, Attending to Its Cost: An Investigation into Generative AI Design for Non-Native Speakers' Language Use

Xiao, Yimin; Hancock, Cartor; Agrawal, Sweta; Mehandru, Nikita; Salehi, Niloufar; Carpuat, Marine; Gao, Ge (April 2025, arXiv)

Free, publicly-accessible full text available April 20, 2026
Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors

https://doi.org/10.18653/v1/2023.emnlp-main.712

Mehandru, Nikita; Agrawal, Sweta; Xiao, Yimin; Gao, Ge; Khoong, Elaine; Carpuat, Marine; Salehi, Niloufar (January 2023, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing)

A major challenge in the practical use of Machine Translation (MT) is that users lack information on translation quality to make informed decisions about how to rely on outputs. Progress in quality estimation research provides techniques to automatically assess MT quality, but these techniques have primarily been evaluated in vitro by comparison against human judgments outside of a specific context of use. This paper evaluates quality estimation feedback in vivo with a human study in realistic high-stakes medical settings. Using Emergency Department discharge instructions, we study how interventions based on quality estimation versus backtranslation assist physicians in deciding whether to show MT outputs to a patient. We find that quality estimation improves appropriate reliance on MT, but backtranslation helps physicians detect more clinically harmful errors that QE alone often misses.
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