Large language models (LLMs) are increasingly used in educational settings where decisions require factual, pedagogical, and value judgments. Existing benchmarks assess general value alignment or educational capability, but rarely evaluate whether model decisions are consistent with a specified value framework in situated educational contexts. We present EduValueBench, an expert-led benchmark of 1,696 decision-oriented items with expert reference decisions and rationales, covering 12 core values in China, seven educational scenario families, and three challenge mechanisms. We evaluated 15 LLMs from China-based providers using a multi-LLM evaluator panel. Target LLMs received only the educational question, without value labels or expert references. Against the expert-authored reference decisions, model scores range from 24.7% to 54.1%, while value- and scenario-level profiles differentiate similarly ranked models. Case analyses show that responses may be fluent and seemingly reasonable yet still optimize a different educational objective or overlook ethical boundaries. Among responses with jointly valid labels, pairwise evaluator agreement ranges from 88.2% to 92.0%. EduValueBench provides a culturally situated diagnostic benchmark for comparing unguided model decisions with expert-authored educational references. Code and data are available at https://github.com/tjunlp-lab/EduValueBench.

“少样本命名实体识别旨在用少量的标注数据来识别命名实体。近年来受提示学习在少样本场景中表现良好性能的启发,本文探索了基于提示的少样本命名实体识别的方法。已有的基于提示学习的方法是通过列举所有可能的跨度来进行实体识别,这导致了计算成本高以及对实体边界信息未充分利用的问题。本文提出一种基于提示学习的两阶段框架TSP-Few,在不使用源域数据的情况下,进行少样本命名实体识别。第一阶段对种子跨度进行增强、过滤和扩展,其中种子增强模块能够让种子跨度捕获到更丰富的语义信息,种子过滤器能够减少大量的无关跨度,种子扩展模块能够充分利用实体的边界信息,为实体类型分类提供高质量的候选实体跨度。第二阶段利用提示学习方法预测候选跨度的相应类别。此外,为了缓解跨度检测阶段的错误累积,在实体分类阶段引入了负采样策略。跨度检测和实体类型分类任务的独立训练更容易在少样本情况下取得优异的性能。在三个基准数据集上的实验表明,与先进的方法相比,本文提出的方法在性能上有了进一步的提升,并且实验结果也表明了该文模型各个模块的有效性。”