教育知识图谱驱动的大规模语料 WoW 发布，覆盖 LLM 全生命周期训练
The WoW: A Large-Scale World Knowledge Corpus for Full-Lifecycle LLM Training
中文教育语料老是被吐槽碎、乱、缺体系，这批人用知识图谱当骨架重建了一遍，还开源了检索平台。做中文模型训练或垂直领域数据构建的可以看看他们的组织思路，比自己乱爬数据靠谱。
研究团队发布 WoW（World Knowledge），一个面向基础模型全周期训练的大规模中文教育语料数据集。该数据集以教育知识图谱为分类骨架，系统化采集数据并补全各学段缺失的教育知识模块，解决了现有中文教育语料覆盖碎片化、组织缺乏体系的问题。相比前代版本，WoW 规模更大、质量更高，并加入多模态信号，支持预训练到后期调优的每个开发阶段。实验显示，使用 WoW 训练的模型在多个基准上取得明显提升。团队还提供了基于图谱的数据选择与检索交互平台，方便研究者按需选取子集。
对中文开源语料生态而言，知识图谱驱动的数据组织方式提供了一条可复用路径：先构建领域骨架，再定位并补齐覆盖缺口，而非单纯堆量。开发者可借助配套平台做图驱动的数据筛选，企业训练垂直模型时也能参考这套体系化构建方法。
The WoW: A Large-Scale World Knowledge Corpus for Full-Lifecycle LLM Training
Existing Chinese educational corpora often suffer from fragmented coverage and a lack of systematic organization. In this paper, we propose WoW (\textbf{Wo}rld Kno\textbf{w}ledge), a large-scale dataset designed for the full-cycle training of foundation models. The construction of this dataset is driven by an Educational Knowledge Graph, which serves as a taxonomic backbone for the systematic collection and completion of missing educational modules across all levels. Compared to previous versions, this iteration provides enhanced systematicity, superior quality, and a larger scale, while incorporating multimodal signals and supporting every stage of model development. Experiments demonstrate that WoW significantly improves model performance across diverse benchmarks. To facilitate community research, we further provide an interactive platform for graph-based data selection and retrieval.
This report is categorized under AITOP's "Papers" desk, focusing on China's AI industry and domestic foundation model ecosystem. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include WoW, 中文语料, 知识图谱, 数据集.
Source attribution: curated from arXiv cs.AI (@Tianze Sun, Yongyao Duan, Sam Chen).