System Report for CCL25-Eval Task 5: New Dataset and LoRA-Fine-Tuned Qwen2.5

Researchers have made notable strides in applying large language models to classical Chinese translation and poetry generation, yet domain-specific studies on precise translation and affective-semantic understanding of classical poetry are scarce. The primary obstacle lies in the treatment of poetic appreciation as a general-domain problem, neglecting the nuances of classical poetry. A new dataset and fine-tuned Qwen2.5 model, leveraging LoRA, have been introduced to address this limitation¹. This development aims to enhance the understanding of classical poetry's emotional and semantic aspects. By focusing on domain-specific research, scholars can uncover more accurate and nuanced translations. The introduction of this new dataset and model has the potential to significantly impact the field of classical Chinese poetry translation. This matters to practitioners because it highlights the need for specialized approaches to fully grasp the complexities of classical poetry, ultimately leading to more sophisticated and culturally sensitive translations.