In2x at WMT25 Translation Task

Lei Pang, Hanyi Mao, Quanjia Xiao, HaiXiao Liu, Xiangyi Li
8/20/2025
cs.CLcs.AI

No confident code implementation found for this paper. 5 possible matches can be shown below.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 5 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Scaling Model and Data for Multilingual Machine Translation with Open Large Language Models

84% match2026

Tower: An Open Multilingual Large Language Model for Translation-Related Tasks

83% match2 repos5902024

LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs

83% match2024

Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study

83% match2025

IKUN for WMT24 General MT Task: LLMs Are here for Multilingual Machine Translation

83% match2024

Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?

83% match2024

Cite this paper

@article{pang2025inx,
  title  = {In2x at WMT25 Translation Task},
  author = {Lei Pang and Hanyi Mao and Quanjia Xiao and HaiXiao Liu and Xiangyi Li},
  year   = {2025},
  eprint = {2508.14472},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2508.14472v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2508.14472.svg)](https://codeofpaper.com/paper/2508.14472)

Discussion