A cross-lingual QA framework shows users build stronger mental models of MT systems through practice and source language knowledge mainly by spotting surface-level errors, with transcriptions helping further.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
AI researchers should take greater responsibility for publicly explaining the limitations of their technologies to prevent misuse in high-stakes applications such as emergency translation services.
citing papers explorer
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Measuring User's Mental Models of Speech Translation in Human-AI Collaboration
A cross-lingual QA framework shows users build stronger mental models of MT systems through practice and source language knowledge mainly by spotting surface-level errors, with transcriptions helping further.
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LLMs in the Real World: Evaluating "AI" in Emergency Contexts
AI researchers should take greater responsibility for publicly explaining the limitations of their technologies to prevent misuse in high-stakes applications such as emergency translation services.