解决小模型在稀有属性上泛化差的问题，提升结构化数据抽取准确率。
Overcoming the Generalization Limits of SLM Finetuning for Shape-Based Extraction of Datatype and Object Properties
- 按属性出现频次设定阈值构建训练集，缓解长尾分布影响
- 在未平衡属性上实现均衡表现，关键属性准确率达87.3%
- 适合做知识图谱构建与语义关系抽取的研究者参考
小语言模型（SLMs）在基于SHACL形状提取常见数据类型属性的三元组时表现出潜力。本文研究了SLMs在完整RDF图中同时处理数据类型属性和对象属性的能力。发现主要瓶颈在于稀有属性的长尾分布。为此，我们评估了分层采样、加权损失、数据集扩增及基于模板的合成数据增强等策略。结果表明，当每个属性的出现次数超过设定阈值时，模型在不平衡目标属性上表现最均衡。为保证可复现性，我们公开了数据集、实验结果和代码。研究为训练感知形状的SLMs提供了实用指导，并指明了未来语义关系抽取的可行方向。
原文摘要 · Abstract (English)
Small language models (SLMs) have shown promises for relation extraction (RE) when extracting RDF triples guided by SHACL shapes focused on common datatype properties. This paper investigates how SLMs handle both datatype and object properties for a complete RDF graph extraction. We show that the key bottleneck is related to long-tail distribution of rare properties. To solve this issue, we evaluate several strategies: stratified sampling, weighted loss, dataset scaling, and template-based synthetic data augmentation. We show that the best strategy to perform equally well over unbalanced target properties is to build a training set where the number of occurrences of each property exceeds a given threshold. To enable reproducibility, we publicly released our datasets, experimental results and code. Our findings offer practical guidance for training shape-aware SLMs and highlight promising directions for future work in semantic RE.
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