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

arXiv: https://arxiv.org/abs/2604.14167 · HTML: https://mlsanity.com/paper/2604.14167/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Making Large Language Models Better Data Creators](https://mlsanity.com/paper/2310.20111/) (2023)
- [ConFiguRe: Exploring Discourse-level Chinese Figures of Speech](https://mlsanity.com/paper/2209.07678/) (2022)
- [CERD: A Comprehensive Chinese Rhetoric Dataset for Rhetorical Understanding and Generation in Essays](https://mlsanity.com/paper/2409.19691/) (2024)

## Abstract (arXiv)

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.