"This paper presents our approach to the Second Chinese Essay Rhetoric Identification and Understanding Competition, which focuses on analyzing rhetorical features in essays written by primary and secondary school students. The competition includes three tasks: multi-label classification of rhetorical forms, divided into 9 coarse-grained and 19 fine-grained categories; multi-label classification of rhetorical content, comprising 5 coarse-grained and 11 fine-grained categories specific to certain rhetorical types; and extraction of rhetorical components, including connectives, descriptive objects, and specific rhetorical content. To address the challenge of limited training data, we applied targeted data augmentation and manual corrections to build a high-quality dataset. We then fine-tuned large language models using one-shot and in-context learning. Finally, we employed an ensemble strategy that integrates model predictions through a voting mechanism. Our system achieved a score of 52.78 and ranked third in the competition."

"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"