让小模型学会听指令，一键切换翻译风格和场景。
On Instruction-Finetuning Neural Machine Translation Models
- 用大模型教小模型听懂指令，实现个性化翻译。
- 单个模型可同时处理正式/非正式、多领域、图文翻译任务。
- 效果接近GPT-3.5-Turbo，推理更快更便宜，适合部署。
本文提出为神经机器翻译（NMT）模型引入指令微调，将大型语言模型（LLMs）的指令遵循能力迁移至规模小一个数量级的NMT模型中。该方法使模型能针对有限但多样化的翻译任务进行定制化调整。我们证明了NMT模型可同时遵循多个指令，并具备零样本指令组合能力。通过指令微调，原本相互独立的任务如形式化控制翻译、多领域适应及多模态翻译，均可由单一指令微调的NMT模型统一处理，性能媲美GPT-3.5-Turbo。据我们所知，这是首次展示传统NMT模型具备指令遵循能力，使得定制化翻译服务在速度、成本与效率上更具优势。
原文摘要 · Abstract (English)
In this work, we introduce instruction finetuning for Neural Machine Translation (NMT) models, which distills instruction following capabilities from Large Language Models (LLMs) into orders-of-magnitude smaller NMT models. Our instruction-finetuning recipe for NMT models enables customization of translations for a limited but disparate set of translation-specific tasks. We show that NMT models are capable of following multiple instructions simultaneously and demonstrate capabilities of zero-shot composition of instructions. We also show that through instruction finetuning, traditionally disparate tasks such as formality-controlled machine translation, multi-domain adaptation as well as multi-modal translations can be tackled jointly by a single instruction finetuned NMT model, at a performance level comparable to LLMs such as GPT-3.5-Turbo. To the best of our knowledge, our work is among the first to demonstrate the instruction-following capabilities of traditional NMT models, which allows for faster, cheaper and more efficient serving of customized translations.
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