MAMMOTH: 赫尔辛基的大规模多语言模块化开放翻译项目
MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki
AI总结：
针对大语言模型时代NLP的规模瓶颈，研究团队推出源自OpenNMT-py的MAMMOTH模块化翻译训练框架，适配集群高效训练，已在A100、V100 GPU集群验证效率并开源。
摘要：
在单一架构大语言模型时代，自然语言处理（NLP）在可处理的模型规模与信息量方面正趋近极限。行业趋势转向模块化，这是朝着设计具备专用功能的更小子网络与组件方向迈进的必要步骤。本文提出MAMMOTH工具包：这是一个用于大规模训练多语言模块化机器翻译系统的框架，最初源自OpenNMT-py，后经适配以确保在计算集群上的高效训练。我们展示了它在配备NVIDIA A100与V100 GPU的集群上的运行效率，并讨论了设计理念与未来规划。该工具包已在线公开可用。
英文摘要：
NLP in the age of monolithic large language models is approaching its limits in terms of size and information that can be handled. The trend goes to modularization, a necessary step into the direction of designing smaller sub-networks and components with specialized functionality. In this paper, we present the MAMMOTH toolkit: a framework designed for training massively multilingual modular machine translation systems at scale, initially derived from OpenNMT-py and then adapted to ensure efficient training across computation clusters. We showcase its efficiency across clusters of A100 and V100 NVIDIA GPUs, and discuss our design philosophy and plans for future information. The toolkit is publicly available online.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
