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        <title>System Report for CCL25-Eval Task 6: Chinese Essay Rhetoric Recognition Using LoRA, In-context Learning and Model Ensemble</title>
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    <abstract>“Rhetoric recognition is a critical component in automated essay scoring. By identifying rhetorical elements in student writing, AI systems can better assess linguistic and higher-order thinking skills, making it an essential task in the area of AI for education. In this paper, we leverage LargeLanguage Models (LLMs) for the Chinese rhetoric recognition task. Specifically, we exploreLow-Rank Adaptation (LoRA) based fine-tuning and in-context learning to integrate rhetoric knowledge into LLMs. We formulate the outputs as JSON to obtain structural outputs and trans-late keys to Chinese. To further enhance the performance, we also investigate several model ensemble methods. Our method achieves the best performance on all three tracks of CCL 2025Chinese essay rhetoric recognition evaluation task, winning the first prize.”</abstract>
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%0 Conference Proceedings
%T System Report for CCL25-Eval Task 6: Chinese Essay Rhetoric Recognition Using LoRA, In-context Learning and Model Ensemble
%A Lai, Yuxuan
%A Wang, Xiajing
%A Zheng, Chen
%Y Lin, Hongfei
%Y Li, Bin
%Y Tan, Hongye
%S Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
%D 2025
%8 August
%I Chinese Information Processing Society of China
%C Jinan, China
%F lai-etal-2025-system
%X “Rhetoric recognition is a critical component in automated essay scoring. By identifying rhetorical elements in student writing, AI systems can better assess linguistic and higher-order thinking skills, making it an essential task in the area of AI for education. In this paper, we leverage LargeLanguage Models (LLMs) for the Chinese rhetoric recognition task. Specifically, we exploreLow-Rank Adaptation (LoRA) based fine-tuning and in-context learning to integrate rhetoric knowledge into LLMs. We formulate the outputs as JSON to obtain structural outputs and trans-late keys to Chinese. To further enhance the performance, we also investigate several model ensemble methods. Our method achieves the best performance on all three tracks of CCL 2025Chinese essay rhetoric recognition evaluation task, winning the first prize.”
%U https://aclanthology.org/2025.ccl-2.27/
%P 220-232

Markdown (Informal)

[System Report for CCL25-Eval Task 6: Chinese Essay Rhetoric Recognition Using LoRA, In-context Learning and Model Ensemble](https://aclanthology.org/2025.ccl-2.27/) (Lai et al., CCL 2025)

• System Report for CCL25-Eval Task 6: Chinese Essay Rhetoric Recognition Using LoRA, In-context Learning and Model Ensemble (Lai et al., CCL 2025)

ACL

• Yuxuan Lai, Xiajing Wang, and Chen Zheng. 2025. System Report for CCL25-Eval Task 6: Chinese Essay Rhetoric Recognition Using LoRA, In-context Learning and Model Ensemble. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 220–232, Jinan, China. Chinese Information Processing Society of China.