Research on the Application of Classical Poetry and Classical-to-Vernacular Translation Under Domain-Specific Large Language Models

Ye Wenhao; Zhao Wenhua; Wang Xiyu; Liu Liu

doi:10.13266/j.issn.0252-3116.2026.17.003

2026 , Vol. 70 >Issue 17: 34 - 49

DOI: https://doi.org/10.13266/j.issn.0252-3116.2026.17.003

SPECIAL TOPIC: Computational Humanities Research on Culture and Classical Texts Supported by Large Language Models

Research on the Application of Classical Poetry and Classical-to-Vernacular Translation Under Domain-Specific Large Language Models

Ye Wenhao ,
Zhao Wenhua ,
Wang Xiyu ,
Liu Liu

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1 College of Information Management, Nanjing Agricultural University, Nanjing 210095;
2 Research Center for Humanities and Social computing of Nanjing Agricultural University, Nanjing 210095

Ye Wenhao, PhD, Postdoc; Zhao Wenhua, Master’s Candidate; Wang Xiyu, Master’s Candidate; Liu Liu, Associate Professor, PhD, Master’s Supervisor, Corresponding Author, E-mail: liuliu@njau.edu.cn.

Received date: 2025-05-07

Revised date: 2025-08-05

Online published: 2026-08-20

Supported by

This work is supported by Major Program of National Social Science Fund of China, titled “Research on the construction and application of cross-language support library for ancient Chinese classics” (Grant No. 21&ZD331); the general project of the Jiangsu Provincial Social Science Fund, titled “Research on the Construction and Application of Knowledge Graphs for Figures in the Twenty-Four Histories” (Grant No. 24TQB004); and the youth project of the Jiangsu Provincial Social Science Fund, titled “Construction and Application of the Knowledge Base for the Translation of Pre-Qin Classics” (Grant No. 23TQC004).

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Abstract

[Purpose/Significance] Large language models provide direct assistance for the machine translation of classical Chinese poetry. This paper aims to examine and compare the performance of large language models in translating classical Chinese poetry, with a particular focus on the impacts of domain-specific models, training dataset sizes, and dynastic periods on translation effectiveness, in order to provide insights for the future optimization of classical poetry translation models. [Method/Process] The study used both holistic and classification methods to construct instruction-tuning datasets, evaluating the models using metrics such as BLEU and ROUGE, as well as agent-based assessments. The models compared include the Qwen1.5-7B-poem, pre-trained and fine-tuned on classical poetry data, as well as the Qwen and Baichuan series large language models. By comparing the translation performance of various models across different dataset sizes, the study analyzed the models' ability to learn linguistic features and translation rules. Additionally, the research compared the translation effectiveness of poems from different dynasties to evaluate the influence of poetic styles and linguistic features on translation quality. [Result/Conclusion] The results show that as the size of the fine-tuning data increases, the translation accuracy and fluency of all models improve significantly, demonstrating their ability to learn the linguistic features and translation rules of classical Chinese poetry, consistent with the scale effect of large language models. The Qwen1.5-7B-poem model performes the best, indicating that specialized large models achieve better scale effects in domain-specific tasks. Moreover, there were significant differences in the translation quality across poems from different dynasties, high-lighting the impact of factors such as temporal linguistic features and poetic styles on model performance. Overall, the performance of large language models in classical Chinese poetry translation is influenced by multiple factors, including the training dataset size and dynastic periods. Future research can further optimize these factors to improve the models' generalization and translation quality.

Key words： large language models; machine translation; classical Chinese poetry; instruction tuning

Cite this article

Ye Wenhao , Zhao Wenhua , Wang Xiyu , Liu Liu . Research on the Application of Classical Poetry and Classical-to-Vernacular Translation Under Domain-Specific Large Language Models[J]. Library and Information Service, 2026 , 70(17) : 34 -49 . DOI: 10.13266/j.issn.0252-3116.2026.17.003

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