Feedback as an Interface: Controlling Form and Detail in LLM Translation Refinement
Feedback presentation shapes how LLMs use evaluator judgments during translation refinement. We introduce a machine translation protocol that keeps the evaluator score, main issue, and recommendation fixed while varying feedback form and supporting detail. Across seven models and six translation directions, low-load feedback outperforms high-load feedback in all 42 translator-direction settings under xCOMET. On the 29,400-record rewriting subset, mean xCOMET scores multiplied by 100 are 83.30 for low-load feedback and 80.67 for high-load feedback; the full-matrix original-translation reference mean is 83.46. MetricX-24 supports the low-load advantage on a stratified subset. A no-feedback ...
| Authors | Yiqi Liu, Flavjo Tomori, Nafise Sadat Moosavi et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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