In2x at WMT25 Translation Task
This work addresses the problem of enabling LLMs to perform well in low-resource languages, which is incremental as it builds on existing translation frameworks.
The paper tackles the challenge of extending large language models to low-resource languages by proposing a generalizable paradigm for Japanese-related translation tasks, achieving exceptional performance as demonstrated in the WMT25 submission.
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.