mlsanityData to 7 Oct 2026

Evaluating the Translation Performance of Large Language Models Based on Euas-20

arXiv 2408.03119 ↗First posted 6 Aug 2024cs.CL cs.AIcited by 2

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Abstract (arXiv)

In recent years, with the rapid development of deep learning technology, large language models (LLMs) such as BERT and GPT have achieved breakthrough results in natural language processing tasks. Machine translation (MT), as one of the core tasks of natural language processing, has also benefited from the development of large language models and achieved a qualitative leap. Despite the significant progress in translation performance achieved by large language models, machine translation still faces many challenges. Therefore, in this paper, we construct the dataset Euas-20 to evaluate the performance of large language models on translation tasks, the translation ability on different languages, and the effect of pre-training data on the translation ability of LLMs for researchers and developers.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 29 references that are in the map. First posted on arXiv 6 Aug 2024.

Builds on

18 of 29 references.

References · 18

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6 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Med-CoReasoner: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoningcited by 2
  2. 2026How Much Do LLMs Know About Chinese Zero Pronouns?cited by 0