WenyanGPT：面向文言文任务的大型语言模型
WenyanGPT: A Large Language Model for Classical Chinese Tasks
AI总结：
本文针对现有模型在文言文任务表现不佳的问题，通过对LLaMA3-8B-Chinese进行持续预训练和指令微调构建了WenyanGPT模型，并开发了评估基准WenyanBENCH，显著提升了文言文任务的性能。
摘要：
文言文作为中华文化的核心载体，在古代文学的传承与研究中发挥着至关重要的作用。然而，现有的自然语言处理模型主要针对现代中文进行优化，导致在文言文上的表现不尽如人意。本文提出了一种文言文语言处理的综合解决方案。通过对LLaMA3-8B-Chinese模型进行持续预训练和指令微调，我们构建了专门为文言文任务设计的大型语言模型WenyanGPT。此外，我们还开发了评估基准数据集WenyanBENCH。在WenyanBENCH上的实验结果表明，WenyanGPT在各种文言文任务中显著优于当前先进的LLMs。我们公开了模型的训练数据、指令微调数据和评估基准数据集，以促进文言文处理领域的进一步研究与发展。
英文摘要：
Classical Chinese, as the core carrier of Chinese culture, plays a crucial role in the inheritance and study of ancient literature. However, existing natural language processing models primarily optimize for Modern Chinese, resulting in inadequate performance on Classical Chinese. This paper presents a comprehensive solution for Classical Chinese language processing. By continuing pre-training and instruction fine-tuning on the LLaMA3-8B-Chinese model, we construct a large language model, WenyanGPT, which is specifically designed for Classical Chinese tasks. Additionally, we develop an evaluation benchmark dataset, WenyanBENCH. Experimental results on WenyanBENCH demonstrate that WenyanGPT significantly outperforms current advanced LLMs in various Classical Chinese tasks. We make the model's training data, instruction fine-tuning data\footnote, and evaluation benchmark dataset publicly available to promote further research and development in the field of Classical Chinese processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
