Activation steering on early layers improves diversity of synthetic data for low-resource languages and often boosts downstream classifier performance compared to non-steered prompting.
Memory-efficient NLLB -200: Language-specific Expert Pruning of a Massively Multilingual Machine Translation Model
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Macro uses DPO on composite preference pairs to raise validity of multilingual self-generated counterfactual explanations by 12.55% on average over chain-of-thought while preserving minimality.
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