CUNI at WMT 25 General Translation Task (2025)

Abstract: This paper describes the CUNI submissions to the WMT25 General Translation task, namely for the English to Czech, English to Serbian, Czech to German and Czech to Ukrainian language pairs. We worked in multiple teams, each with a different approach, spanning from traditional, smaller Transformer NMT models trained on both sentence and document level, to fine-tuning LLMs using LoRA and CPO. We show that these methods are effective in improving automatic MT evaluation scores compared to the base pretrained models.

## introduction we have entered the shared task as a number of small teams with different approaches, each with its own submission. we will describe the datasets, methods and evaluation results of each submission in the following sections. here we will present just a brief overview of all the systems we submitted. cuni-mh-v2 is a constrained system trained on partially synthetic data sampled from the czeng 2.0 @xcite dataset using lora @xcite and contrastive preference optimization @xcite . we will release both the model weights and the filtered training data. the model itself is fine-tuned from the eurollm-9b-instruct model. we currently only support two language directions, (@xmath0, and offer separate lora adapters for each. the translations were done on the paragraph level. cuni-edukate-v1 is an unconstrained system trained on educational domain data using lora, sft, and contrastive preference optimization. it is also fine-tuned from the eurollm-9b-instruct model. it only supports cs2uk language direction and, unlike cuni-mh-v2, both training and inference were done on sentence level. cuni-sft models were created by a simple supervised finetuning using lora on a small amount of publicly available training data. cuni-transformer and cuni-doctransformer are resubmissions of systems from previous years. ## methods this section describes the approaches used for training our submissions. ## tools to give a proper credit, we list the tools we used during the development and inference with our ## conclusion in this paper, we presented the cuni submissions to the wmt25 general translation task, covering @xmath1, @xmath2, @xmath3, and @xmath4. future work will focus on scaling preference datasets and leveraging longer-context translation scenarios.