Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation (2025.emnlp-main)
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| Challenge: | Existing studies on the impact of feedback on human decision-making are limited as people are not equipped to assess the quality of AI predictions. |
| Approach: | They compare the quality of MT inputs and outputs with explicit and implicit feedbacks that directly give users an assessment of translation quality using error highlights and LLM explanations. |
| Outcome: | The proposed model improves decision accuracy and appropriate reliance by using error highlights and explanations, and by using backtranslation and question–answer tables. |
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