In2x团队在WMT25中探索LLM扩展至日语等低资源语言的新范式。
In2x at WMT25 Translation Task
- 构建适配多语言的数据与奖励模型体系
- 针对日语翻译任务实现高性能表现
- 适合关注低资源语言模型落地的研究者
本文介绍In2x研究团队在WMT25通用机器翻译共享任务中的开放系统提交。该提交聚焦日语相关翻译任务，旨在探索将大语言模型（LLMs）推广至其他语言的通用范式。该范式涵盖数据构建方法与奖励模型设计等方面，目标是使大语言模型系统在低资源或较少使用语言中实现卓越性能。
原文摘要 · Abstract (English)
This paper presents the open-system submission by the In2x research team for the WMT25 General Machine Translation Shared Task. Our submission focuses on Japanese-related translation tasks, aiming to explore a generalizable paradigm for extending large language models (LLMs) to other languages. This paradigm encompasses aspects such as data construction methods and reward model design. The ultimate goal is to enable large language model systems to achieve exceptional performance in low-resource or less commonly spoken languages.
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