Biomedical translation models compressed using distillation and quantization

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员探索了结合知识蒸馏和量化技术来压缩生物医学领域的神经机器翻译模型。该方法旨在为专业翻译任务创建更小、更快的模型，尤其是在平行数据稀缺的情况下。实验表明，使用这两种技术的学生模型在法语到英语的生物医学翻译中，在减小模型尺寸和二氧化碳排放方面取得了显著成效，同时保持了翻译质量。 AI

影响能够更有效地部署专业翻译模型，降低计算成本和环境影响。

排序理由该集群包含一篇研究论文，详细介绍了特定领域模型压缩的新颖方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Maria Zafar, Souhail Bakkali, Rejwanul Haque · 2026-10-07 04:00

生物医学领域神经机器翻译的模型压缩研究

arXiv:2610.07032v1 Announce Type: cross Abstract: Large-scale pretrained transformer models have achieved state-of-the-art performance across diverse machine translation tasks, including multilingual settings. Knowledge distillation has emerged as a sustainable approach for model…