As large language models (LLM) become more and more capable in languages other than English, it is important to collect benchmark datasets in order to evaluate their multilingual performance, including on tasks like machine translation (MT). In this work, we extend the WMT24 dataset to cover 55 languages by collecting new human-written references and post-edits for 46 new languages/dialects in addition to post-edits of the references in 8 out of 9 languages in the original WMT24 dataset. We benchmark a variety of MT providers and LLMs on the collected dataset using automatic metrics and find that LLMs are the best-performing MT systems in all 55 languages. However, we caution against using our results to reach strong conclusions about MT quality without a human-based evaluation due to limitations of automatic evaluation metrics, which we leave for future work.

Current machine translation (MT) systems for low-resource languages have a particular failure mode: When translating words in a given domain, they tend to confuse words within that domain. So, for example, “lion” might be translated as “alligator”, and “orange” might be rendered as “purple.” We propose a recall-based metric for measuring this problem and show that the problem exists in 122 low-resource languages. We then show that this problem can be mitigated by using a large language model (LLM) to post-edit the MT output, specifically by including the entire GATITOS lexicon for the relevant language as a very long context prompt. We show gains in average ChrF score over the set of 122 languages, and we show that the recall score for relevant lexical items also improves. Finally, we demonstrate that a small dedicated MT system with a general-purpose LLM as a post-editor is outperforms a lexicon-based RAG-LLM translator, suggesting a new paradigm for LLM use.