Papers by Xinhao Wu
Convolutional LoRA Aggregation for Unseen Tasks Adaptation (2025.findings-emnlp)
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| Challenge: | Existing LoRA selection methods rely on a few task samples, making it difficult to capture full scope of task-relevant information. |
| Approach: | They propose a framework that selects appropriate LoRA modules and aggregates them using a convolutional LoRA aggregator. |
| Outcome: | The proposed framework bridges the knowledge gap between selected modules and target task . it ensures comprehensive coverage of task-relevant LoRA modules . |
CERD: A Comprehensive Chinese Rhetoric Dataset for Rhetorical Understanding and Generation in Essays (2024.findings-emnlp)
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Nuowei Liu, Xinhao Chen, Hongyi Wu, Changzhi Sun, Man Lan, Yuanbin Wu, Xiaopeng Bai, Shaoguang Mao, Yan Xia
| Challenge: | Existing rhetorical understanding and generation datasets focus on single coarse-grained categories or fine-grain categories, neglecting the intrinsic connections between different rhetorical devices. |
| Approach: | They propose a Chinese Essay Rhetoric Dataset with four coarse-grained categories . they propose to treat these categories as separate sub-tasks, thereby improving writing skills . |
| Outcome: | The proposed dataset improves the author's writing proficiency and language usage skills by recognizing and generating rhetorical sentences under given conditions. |
TOREE: Evaluating Topic Relevance of Student Essays for Chinese Primary and Middle School Education (2024.findings-acl)
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Xinlin Zhuang, Hongyi Wu, Xinshu Shen, Peimin Yu, Gaowei Yi, Xinhao Chen, Tu Hu, Yang Chen, Yupei Ren, Yadong Zhang, Youqi Song, Binxuan Liu, Man Lan
| Challenge: | Existing research on Automatic Essay Scoring (AES) for Chinese essays has overlooked topic relevance and lacks detailed feedback. |
| Approach: | They propose to use TOREE to assess topic relevance in Chinese primary and middle school students’ essays to improve automatic and human evaluations. |
| Outcome: | The proposed method significantly improves both automatic and human evaluations across four diverse LLMs. |