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Findings of the WMT26 Shared Task: Multitask LLMs With Limited Resources

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Link to Profile Alexander Fraser

Alexander Fraser

Prof. Dr.

Core PI

Data Analytics & Statistics

Abstract

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.

inproceedings OMH+26

WMT 2026

11th Conference on Machine Translation. Budapest, Hungary, Oct 28-29, 2026. To be published. Preprint available.

Authors

S. Okabe • M. Di Marco • K. Hämmerl • D. Dementieva • L. Edman • M. Meškank • A. Hendrichowa • A. Fraser

Links

URL

Research Area

 B2 | Natural Language Processing

BibTeXKey: OMH+26

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