Chinese Essay Rhetoric Recognition Using LoRA, In-context Learning and Model Ensemble

arXiv cs.AI / 4/17/2026

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Key Points

The paper addresses Chinese rhetoric recognition for automated essay scoring by using LLMs to detect rhetorical elements and support evaluation of both language and higher-order thinking skills.
It proposes integrating rhetoric knowledge into LLMs via LoRA-based fine-tuning and in-context learning, with outputs structured as JSON and key fields translated into Chinese.
To improve results further, the authors test multiple model ensemble strategies alongside their core LLM approach.
The proposed system achieves top performance across all three tracks of the CCL 2025 Chinese essay rhetoric recognition evaluation and wins first prize.

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 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.