用LoRA和上下文学习提升中文作文修辞识别准确率
Chinese Essay Rhetoric Recognition Using LoRA, In-context Learning and Model Ensemble
- 结合LoRA微调与上下文学习注入修辞知识
- 在CCL 2025三项任务中均获最佳表现
- 适合教育AI、自动作文评分系统参考
修辞识别是自动作文评分中的关键环节。通过识别学生作文中的修辞元素，人工智能系统可更准确评估语言能力与高阶思维水平，对教育AI至关重要。本文针对中文修辞识别任务，利用大语言模型（LLMs）进行建模。具体采用基于低秩适配（LoRA）的微调与上下文学习方法，将修辞知识融入模型。输出格式化为JSON以获得结构化结果，并将键名翻译为中文。为进一步提升性能，还探索多种模型集成方法。该方法在CCL 2025中文作文修辞识别评测的三个赛道上均取得最优成绩，荣获一等奖。
原文摘要 · Abstract (English)
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 Large Language Models (LLMs) for the Chinese rhetoric recognition task. Specifically, we explore Low-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 translate 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 2025 Chinese essay rhetoric recognition evaluation task, winning the first prize.
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