Abstract：General-purpose large language models demonstrate notable capabilities in language comprehension and generation, achieving results comparable to or surpassing human performance in many language information processing tasks. Nevertheless, when general models are applied to ancient Chinese texts, their effectiveness is often unsatisfactory. Similarly, fine-tuning open-source foundational models encounter difficulties in adequately incorporating domain-specific knowledge. To address this challenge, this study introduces a large language model, AI Taiyan, specifically designed for understanding and generating ancient Chinese. Experimental results show that with an appropriately designed model, data processing, foundational training, and fine-tuning, satisfactory results can be achieved with merely 1.8 billion parameters. In key tasks related to ancient Chinese information processing such as punctuation, identification of allusions, explanation of word meanings, and translation between ancient and modern Chinese, this model exhibits a clear advantage over both general-purpose large models and domain-specific models, achieving or surpassing human baseline performance. This research provides a reference framework for the efficient construction of specialized domain-specific large language models. Furthermore, the paper discusses the application of this model in fields such as the collation of ancient texts, dictionary editing, and language research, supplemented by case studies.
李 绅，胡韧奋，王立军. 古汉语大语言模型的构建及应用研究[J]. 语言战略研究, 2024, 9(5): 22-33.
Li Shen, Hu Renfen and Wang Lijun. Construction and Application of Ancient Chinese Large Language Model. , 2024, 9(5): 22-33.
