做古典文学 NLP 或诗歌 AI 应用的团队终于有了专用数据集和微调方案，PoetryQwen 在翻译和情感理解上提升明显，值得直接参考使用。
针对古诗鉴赏任务中领域特定数据集稀缺、通用模型忽视诗歌特性的问题，研究团队构建了包含 49,404 条指令-响应对的 CCPoetry-49K 数据集，覆盖术语解释、语义理解和情感推理三个子任务。基于该数据集，通过 LoRA 微调 Qwen2.5-14B 得到 PoetryQwen 模型，在 CCL25-Eval Task 5 基准上达到 0.757 分，相比基线提升 9.7%。该工作为古诗精准翻译与情感理解提供了专用数据集和优化方法，展示了领域微调的有效性。
System Report for CCL25-Eval Task 5: New Dataset and LoRA-Fine-Tuned Qwen2.5
Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry. However, domain-specific research on precise translation and affective-semantic understanding of classical poetry remains limited. The main challenge is that most studies treat the poetic appreciation task as a general-domain problem, neglecting the distinctive features of poetic appreciation, while high-quality and domain-specific datasets are extremely limited. To address this limitation, we decompose the task into three subtasks: term interpretation, semantic interpretation, and emotional inference. Based on multiple open-source datasets, we perform data cleansing and alignment to construct the Classical Chinese Poetry Instruction Pair Dataset (CCPoetry-49K), which comprises 49,404 high-quality instruction-response pairs explicitly optimized for this domain. We then propose a domain-specialized LLM, called PoetryQwen, by applying Low-Rank Adaptation (LoRA) to fine-tune the Qwen2.5-14B model. Experimental results on the CCL25-Eval Task 5 benchmark demonstrate that PoetryQwen achieves a score of 0.757, representing a 9.7% improvement over the Qwen2.5-14B-Instruct baseline (0.690). These findings clearly indicate that PoetryQwen significantly enhances performance in precise translation and emotional understanding of classical poetry. We present new dataset and methodological considerations intended to support the domain-specific optimization of LLMs.