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        <title>Translating Ancient Chinese to Modern Chinese at Scale: A Large Language Model-based Approach</title>
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            <title>Proceedings of ALT2023: Ancient Language Translation Workshop</title>
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    <abstract>Recently, the emergence of large language models (LLMs) has provided powerful foundation models for a wide range of natural language processing (NLP) tasks. However, the vast majority of the pre-training corpus for most existing LLMs is in English, resulting in their Chinese proficiency falling far behind that of English. Furthermore, ancient Chinese has a much larger vocabulary and less available corpus than modern Chinese, which significantly challenges the generalization capacity of existing LLMs. In this paper, we investigate the Ancient-Chinese-to-Modern-Chinese (A2M) translation using LLMs including LLaMA and Ziya. Specifically, to improve the understanding of Chinese texts, we explore the vocabulary expansion and incremental pre-training methods based on existing pre-trained LLMs. Subsequently, a large-scale A2M translation dataset with 4M pairs is utilized to finetune the LLMs.Experimental results demonstrate the effectiveness of the proposed method, especially with Ziya-13B, in translating ancient Chinese to modern Chinese. Moreover,we deeply analyze the performance of various LLMs with different strategies, which we believe can benefit further research on LLM-based A2M approaches.</abstract>
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%0 Conference Proceedings
%T Translating Ancient Chinese to Modern Chinese at Scale: A Large Language Model-based Approach
%A Cao, Jiahuan
%A Peng, Dezhi
%A Shi, Yongxin
%A Jiang, Zongyuan
%A Jin, Lianwen
%S Proceedings of ALT2023: Ancient Language Translation Workshop
%D 2023
%8 September
%I Asia-Pacific Association for Machine Translation
%C Macau SAR, China
%F cao-etal-2023-translating
%X Recently, the emergence of large language models (LLMs) has provided powerful foundation models for a wide range of natural language processing (NLP) tasks. However, the vast majority of the pre-training corpus for most existing LLMs is in English, resulting in their Chinese proficiency falling far behind that of English. Furthermore, ancient Chinese has a much larger vocabulary and less available corpus than modern Chinese, which significantly challenges the generalization capacity of existing LLMs. In this paper, we investigate the Ancient-Chinese-to-Modern-Chinese (A2M) translation using LLMs including LLaMA and Ziya. Specifically, to improve the understanding of Chinese texts, we explore the vocabulary expansion and incremental pre-training methods based on existing pre-trained LLMs. Subsequently, a large-scale A2M translation dataset with 4M pairs is utilized to finetune the LLMs.Experimental results demonstrate the effectiveness of the proposed method, especially with Ziya-13B, in translating ancient Chinese to modern Chinese. Moreover,we deeply analyze the performance of various LLMs with different strategies, which we believe can benefit further research on LLM-based A2M approaches.
%U https://aclanthology.org/2023.alt-1.9
%P 61-69

Markdown (Informal)

[Translating Ancient Chinese to Modern Chinese at Scale: A Large Language Model-based Approach](https://aclanthology.org/2023.alt-1.9) (Cao et al., alt 2023)

• Translating Ancient Chinese to Modern Chinese at Scale: A Large Language Model-based Approach (Cao et al., alt 2023)

ACL

• Jiahuan Cao, Dezhi Peng, Yongxin Shi, Zongyuan Jiang, and Lianwen Jin. 2023. Translating Ancient Chinese to Modern Chinese at Scale: A Large Language Model-based Approach. In Proceedings of ALT2023: Ancient Language Translation Workshop, pages 61–69, Macau SAR, China. Asia-Pacific Association for Machine Translation.