alleryStart learning →Machine translation many-shot results
ACannot be evaluated due to lack of reference data
BBeat NLLB and Google Translate on Bemba and Kurdish
CUnderperform NLLB by a wide margin across languages
DMatch few-shot exactly without measurable gains
Answer & Solution
Correct answer: B. Beat NLLB and Google Translate on Bemba and Kurdish
Using the entire FLORES dev set (997 shots ≈ 85k tokens) yields 15.3% gain on Bemba and 4.5% on Kurdish over 1-shot, establishing a new state of the art for these low-resource pairs.
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