@InProceedings{wu-EtAl:2026:wmt,
  author    = {Wu, Di  and  Troshin, Sergey  and  Mohammed, Wafaa  and  Aycock, Seth  and  Tokarchuk, Evgeniia  and  Niculae, Vlad  and  Monz, Christof},
  title     = {UvA-MT's Participation in the WMT26 General Translation Shared Task},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {1447--1453},
  abstract  = {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. Specifically, (1) we use sequential sampling with a refinement prompt to generate a pool of translation candidates for each source sentence, a strategy shown to outperform traditional parallel sampling; and (2) we employ LLMs as pairwise judges to select the best candidates via a round-robin voting mechanism, with offline evaluation on the WMT25 benchmark showing a very clear improvement over point-wise best-of-N selection. Lastly, we simply apply these two strategies for the submission of WMT26.},
  url       = {https://aclanthology.org/2026.wmt-1.82}
}