Automatic translation system from Classical Chinese to Modern Chinese based on large language models

This study explores the application of large pre-trained language models in Classical Chinese translation, aiming to address the semantic understanding shortcomings of traditional methods. The LoRA-BLOOMZ model structure is employed, and experiments demonstrate that structured prompting along with the integration of LoRA technology and BLOOM model structure can enhance the performance of Classical Chinese translation. Through appropriate training strategies, stable training and optimized performance of the model are ensured, thereby achieving favorable BLEU scores in the Classical Chinese translation task. This study offers an effective new approach for Classical Chinese translation, providing a reference for the further application of large pre-trained language models.