Mufu：面向低资源翻译与LLM的多语言融合学习
Mufu: Multilingual Fused Learning for Low-Resource Translation with LLM
- The University of Melbourne（墨尔本大学）
- Google（谷歌）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
Mufu通过自动生成多语言候选译文并将翻译任务转化为译后编辑，利用LLM推理能力提升低资源翻译质量，在Flores-200上64%的低资源语言对中超越NLLB 1.3B蒸馏模型，蒸馏后仍平均提升3.1 chrF。
AI中文摘要：
多语言大语言模型（LLMs）是出色的翻译器，但这在很大程度上仅限于高资源语言。对许多LLM而言，在低资源语言之间进行翻译仍然是一项具有挑战性的任务。为了在这种低资源环境下最大化数据效率，我们提出了Mufu，该方法包含一组自动生成的多语言候选译文，以及在提示中纠正不准确译文的指令。Mufu提示将翻译任务转变为译后编辑任务，并力求利用LLM的推理能力处理辅助翻译候选，模型需要据此评估输入质量、跨语言对齐语义、从相关输入中复制内容，并覆盖不正确的实例。我们在Flores-200数据集上的En-XX翻译实验表明，针对Mufu风格提示进行微调的LLM对低质量辅助翻译候选具有鲁棒性，在64%的低资源和极低资源语言对中取得了优于NLLB 1.3B蒸馏模型的性能。随后我们对这些模型进行蒸馏以降低推理成本，同时在低资源翻译中平均比仅微调基线提高3.1 chrF。
英文摘要：
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 low-resource languages remains a challenging task. To maximize data efficiency 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.
