Evaluating the Translation Performance of Large Language Models Based on Euas-20
This work addresses evaluation needs for researchers and developers working on LLM-based machine translation, but appears incremental as it focuses on dataset creation rather than novel methods.
The paper tackles the challenge of evaluating translation performance of large language models (LLMs) by constructing the Euas-20 dataset to assess translation ability across different languages and the impact of pre-training data, but no specific results or numbers are provided.
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.