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Instruction tuning can reduce masculine bias in decoder-only MT models, but these models still don't consistently outperform encoder-decoder architectures on gender-specific translation tasks.
Current machine translation systems exhibit systematic masculine overuse and inconsistent feminine realization when translating from gender-neutral languages, a problem that can now be quantified thanks to a new gold-standard annotation framework.
