@inproceedings{okabe-etal-2026-findings,
title = "Findings of the {WMT} 2026 Shared Task: Multitask {LLM}s with Limited Resources",
author = "Okabe, Shu and
Di Marco, Marion and
Haemmerl, Kathy and
Dementieva, Daryna and
Edman, Lukas and
M{\v{e}}{\v{s}}kank, Marko and
Hendrichowa, Anita and
Fraser, Alexander",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wmt-main.59/",
pages = "1180--1202",
ISBN = "979-8-89176-474-3",
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.\par 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."
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<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.\par 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.</abstract>
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%0 Conference Proceedings
%T Findings of the WMT 2026 Shared Task: Multitask LLMs with Limited Resources
%A Okabe, Shu
%A Di Marco, Marion
%A Haemmerl, Kathy
%A Dementieva, Daryna
%A Edman, Lukas
%A Měškank, Marko
%A Hendrichowa, Anita
%A Fraser, Alexander
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F okabe-etal-2026-findings
%X 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.\par 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.
%U https://aclanthology.org/2026.wmt-main.59/
%P 1180-1202
Markdown (Informal)
[Findings of the WMT 2026 Shared Task: Multitask LLMs with Limited Resources](https://aclanthology.org/2026.wmt-main.59/) (Okabe et al., WMT 2026)
ACL
- Shu Okabe, Marion Di Marco, Kathy Haemmerl, Daryna Dementieva, Lukas Edman, Marko Měškank, Anita Hendrichowa, and Alexander Fraser. 2026. Findings of the WMT 2026 Shared Task: Multitask LLMs with Limited Resources. In Proceedings of the Eleventh Conference on Machine Translation, pages 1180–1202, Budapest, Hungary. Association for Computational Linguistics.