Mufu: Multilingual Fused Learning for Low-Resource Translation with LLM

Multilingual large language models (LLMs) are great translators, but this is largely limited to high-resource languages. For many LLMs, translating in and out of lowresource languages remains a challenging task. To maximize data e ciency in this low-resource setting, we introduce Mufu, which includes a selection of automatically generated multilingual candidates and an instruction to correct inaccurate translations in the prompt. Mufu prompts turn a translation task into a postediting one, and seek to harness the LLM's reasoning capability with auxiliary translation candidates, from which the model is required to assess the input quality, align the semantics cross-lingually, copy from relevant inputs and override instances that are incorrect. Our experiments on En-XX translations over the Flores-200 dataset show LLMs finetuned against Mufu-style prompts are robust to poor quality auxiliary translation candidates, achieving performance superior to NLLB 1.3B distilled model in 64% of low-and very-low-resource language pairs. We then distill these models to reduce inference cost, while maintaining on average 3.1 chrF improvement over finetune-only baseline in low-resource translations. This performance gap is caused primarily by scant pre-training data in these languages (Wei et al., 2023; Yuan et al., 2024; Alves et al., 2024), and is di cult to overcome despite growing e orts to support translations of long-tail languages (Kudugunta et al., 2024; Bapna et al., 2022; Lu et al., 2024). In this work, we introduce multilingual fused learning (Mufu), which combines multilingual context and a postediting task when translating into lower-resource languages using LLMs.1 Mufu-style prompts (see Table 1, top block) include several multilingual translation candidates along with a postediting target, from which a model learns "in-context" to translate from languages with which the target language is more closely aligned due to cultural relevance, geographical and genealogical proximity. We rely on a larger, more competent multilingual teacher model to generate auxiliary translations in these languages, which help disambiguate inputs and improve cross-lingual semantic alignment in a translation task.