We present the Findings of the WMT 2026 Shared Task on Multitask LLMs with Limited Resources. This year, we extended the Shared Task to jointly model five diverse NLP tasks, Machine Translation (MT) for five language pairs (eight directions), Question Answering (QA), Spell Checking (SC), Grammar Checking (GC), and Mathematical Reasoning (MR), for two language tracks: Ukrainian and Sorbian (grouping Upper and Lower Sorbian). As the Shared Task is focused on low-resource conditions, the model is restricted to Qwen3.5-2B to remain below the 3B-parameter threshold.

In total, six teams participated across the two language tracks: two in the Ukrainian and four in the Sorbian track. No team participated in both language tracks. All systems have been uploaded to HuggingFace by the participants. Despite our strict restrictions on model and dataset availability, systems were diverse in the resources they used as well as their training and inference strategies. The submitted systems show that a single small model can handle all five tasks, with clear difficulty shown for the challenging MR only.

We present the findings of the WMT 2025 Shared Task LLMs with Limited Resources for Slavic Languages. This shared task focuses on training LLMs using limited data and compute resources for three Slavic languages: Upper Sorbian (hsb), Lower Sorbian (dsb), and Ukrainian (uk), with the objective to develop and improve LLMs for these languages. We consider two tasks which are to be evaluated jointly: Machine Translation (MT) and Multiple-Choice Question Answering (QA). In total, three teams participated in this shared task, with submissions from all three teams for the Sorbian languages and one submission for Ukrainian. All submissions led to an improvement compared to the baseline Qwen2.5-3B model through varying fine-tuning strategies. We note, however, that training purely on MT degrades original QA capabilities. We also report further analyses on the submissions, including MT evaluation using advanced neural metrics for Ukrainian, as well as manual annotation and comparison to the current Sorbian machine translator.