“Natural language processing technology has been widely applied in the field of education. Essay writing serves as a crucial method for evaluating students’ language skills and logical thinking abilities. Rhetoric, an essential component of essay, is also a key reference for assessing writing quality. In the era of large language models (LLMs), applying LLMs to the tasks of automatic classification and extraction of rhetorical devices is of significant importance. In this paper, we fine-tune LLMs with specific instructions to adapt them for the tasks of recognizing and extracting rhetorical devices in essays. To further enhance the performance of LLMs, we experimented with multi-task fine-tuning and expanded the training dataset through synthetic data. Additionally, we explored a model ensemble approach based on label re-inference. Our method achieved a score of 66.29 in Task 6 of the CCL 2024 Eval, Chinese Essay Rhetoric Recognition and Understanding(CERRU), securing the first position.”

“尽管现有语言模型在自然语言处理任务上表现出色,但在深层次语义理解和常识推理方面仍有提升空间。本研究通过测试模型在儿童故事常识推理与寓意理解数据集(CRMUS)上的性能,探究如何增强模型在复杂任务中的能力。在本次任务的赛道二中,本研究使用多个7B以内的开源大模型(如Qwen、InternLM等)进行零样本推理,并选择表现最优的模型基于LoRA进行指令微调来提高其表现。除此之外,本研究还对数据集进行了分析与增强。研究结果显示,通过设计有效的指令格式和调整LoRA微调参数,模型在常识推理和寓意理解上的准确率显著提高。最终在本次任务的赛道二中取得第一名的成绩,该任务的评价指标Acc值为74.38,达到了较为先进的水准。”