“本研究的任务旨在让大模型进行实体识别、角色识别、异常识别、信息推理、同义识别任务,综合评估大模型的空间语义理解能力。其中,我们使用普通提示词、工作流提示词和思维链三种提示词策略来探讨大模型的空间语义理解能力,最后发现ERNIE-4在1-shot的普通提示词上表现最佳。最终,我们的方法排名第六,总体准确率得分为56.20%。”

This paper describes the system submitted for the EvaHan 2024 Task on ancient Chinese sentence segmentation and punctuation. Our study utillizes the Xunzi large language model as the base model to evaluate the overall performance and the performance by record type. The applied methodologies and the prompts utilized in our study have shown to be helpful and effective in aiding the model’s performance evaluation.