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.

Learned quality estimation (QE) models such as COMETKiwi are widespread and work well for general machine translation evaluation. However, they are known to struggle on unseen domains, limiting their performance in a real-world localisation context. We show that they are insensitive to some important factors in localisation, such as whether numbers are translated accurately, or even whether the correct number of spaces and punctuation are preserved in a translation. Further, a key capability for optimisation of machine translation is the ability of QE models to accurately rank different translations of a single segment, which suffers significantly from the domain transfer. In the absence of large-scale direct assessment data, we propose principled fine-tuning approaches to reduce the domain gap with even small amounts of post-editing data. Using a multi-task fine-tuning approach and a simple tokeniser intervention, we create a QE model which proves markedly better at distinguishing preferred post-edits from rejected initial translations in a localisation context. We show that preferences and artificial continuous scores stabilise each other, and argue that to calibrate metrics both in terms of their absolute scores and comparisons between translation of the same source, both types of signal are needed.