"汉语框架语义解析基于框架语义学理论,旨在通过识别句子中词语所激活的语义框架, 分析句子中各个成分的语义角色, 从而揭示语言背后的深层语义结构,进一步更好地抽取事件关系和语境信息。 大语言模型出现后,其强大的通用文本理解与生成能力被广泛应用于各种自然语言处理任务中。 然而,当前大语言模型在汉语框架语义解析任务中存在推理路径简单、 准确率过低的不足,尤其在思维链的逻辑连贯性和检索增强生成的深度应用上存在欠缺。 为此,本文提出了一种面向汉语框架语义解析的思维提示方法。 该方法结合检索增强生成(RAG)与链式思维(CoT)技术,引导大语言模型完成汉语框架语义解析任务。我们在CFN2.1数据集上的实验结果表明,与最好方法相比,该方法的框架识别准确率提升13.52%,论元识别F1提升2.24%,角色识别F1提升5.09%。"

Large language models (LLMs) achieve remarkable performance across various domains, largely due to training on massive datasets. However, this also raises growing concerns over the exposure of sensitive and private information, making model unlearning increasingly critical.However, existing methods often struggle to balance effective forgetting with maintaining model utility. In this work, we propose HyperUnlearn, a human-inspired unlearning framework. We construct two types of fuzzy data—local and global—to simulate forgetting, and represent them in hyperbolic and Euclidean spaces, respectively. Unlearning is performed on a model with frozen early layers to isolate forgetting and preserve useful knowledge.Experiments demonstrate that HyperUnlearn effectively forgets sensitive content while maintaining the model’s language understanding, fluency, and benchmark performance, offering a practical trade-off between forgetting and capability preservation.

This paper describes our system used in SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models. In this work, we propose a method for controlling the fine-tuning of a model’s linear layers, referred to as CTL-Finetune (Control-Tuned Linear Fine-tuning). The goal of our method is to allow the model to forget specific information while preserving the knowledge it needs to retain. The method consists of four main components: 1) shuffling data labels, 2) shuffling label gradient calculation, 3) determination of control layers, and 4) fine-tuning using a combination of gradient ascent and gradient descent. Experimental results demonstrate that our approach effectively enables the model to forget targeted knowledge while minimizing the impact on retained information, thus maintaining the model’s overall performance.