"Literary grace in Chinese composition writing is a hallmark of linguistic sophistication, often realized through various rhetorical devices. The automatic identification and analysis of rhetorical devices in essays play a crucial role in educational NLP applications, particularly for assessing writing proficiency and facilitating pedagogical interventions. Although prior research has predominantly focused on coarse-grained recognition of limited rhetorical devices at sentence level, these approaches prove inadequate for handling complex rhetorical structures and emerging educational demands. In this paper, we present the CCL25-Eval Task6: Chinese EssayRhetoric Recognition Evaluation (CERRE), a novel framework comprising three distinct evaluation tracks at the document level: (1) Fine-grained Form-level Categories Recognition, (2)Fine-grained Content-level Categories Recognition, and (3) Rhetorical Component Extraction.The evaluation has attracted 29 registered participating teams, with 8 teams submitting valid system outputs. In particular, two participating systems demonstrated superior performance by exceeding the baseline metrics in complete evaluation criteria."

This paper explores the application of fine-tuning methods based on 7B large language models (LLMs) for named entity recognition (NER) tasks in Chinese ancient texts. Targeting the complex semantics and domain-specific characteristics of ancient texts, particularly in Traditional Chinese Medicine (TCM) texts, we propose a comprehensive fine-tuning and pre-training strategy. By introducing multi-task learning, domain-specific pre-training, and efficient fine-tuning techniques based on LoRA, we achieved significant performance improvements in ancient text NER tasks. Experimental results show that the pre-trained and fine-tuned 7B model achieved an F1 score of 0.93, significantly outperforming general-purpose large language models.