UvA-MT's Participation in the WMT26 General Translation Shared Task
This paper presents UvA-MT's submission to the WMT 2026 General Machine Translation shared task, competing in the unconstrained track across all 23 translation directions.
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Key points
- This year, we fully leverage the test-time methods of Large Language Models (LLMs) for machine translation.
- Combined, these two strategies form our WMT26 submission.
Sources (1)
- [1]UvA-MT's Participation in the WMT26 General Translation Shared TaskarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 08:33 AM
This paper presents UvA-MT's submission to the WMT 2026 General Machine Translation shared task, competing in the unconstrained track across all 23 translation directions.
This year, we fully leverage the test-time methods of Large Language Models (LLMs) for machine translation.
Extractive summary: sentences quoted from the sources.
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