Abstract

"We present a hierarchical multi-task framework to enhance classical Chinese poetry understand-ing and sentiment reasoning using large language models. Centered on Qwen2.5-14B-Instruction or Xunzi-Qwen-14B, we construct a 1,225-sample corpus of Tang and Song poems with parallel translations and multi-label sentiment annotations (e.g., nostalgia, patriotism, contemplation).The task is divided into comprehension, translation, and sentiment inference, each guided by dynamic prompting and task-specific templates. We employ mixed supervised fine-tuning to better capture syntactic and metaphorical patterns. For sentiment reasoning, we apply proximal policy optimization (PPO) with a custom reward function, boosting accuracy from 0.771 to 0.807(p < 0.01). Our model achieves a 0.714 comprehensive score, outperforming single-task base-lines by 12.6%. Ablation studies further confirm the benefits of multi-task learning in promoting cross-task knowledge transfer.Keywords: Classical Chinese Poetry, Multi-Task Fine-Tuning, Data Augmentation, ProximalPolicy Optimization"