K12-Train¶
Product or Programme · All products and programmes
Evidence · 3 claims (3 for) · 1 study (1 design),q2· 0 of 1 report an effect size · 3 claims rest on one study
Description¶
K12-Train is a knowledge-graph-guided dataset and generation pipeline by Hao Liang et al. that produces validated text and multimodal K–12 STEM question-answer samples for supervised fine-tuning.
Components¶
Claims¶
- K12-Train-Full achieves the best overall performance on three multimodal educational benchmarks, and textual and visual supervision are complementary [+M]
- Under a matched 2,300-sample SFT budget, K12-Train-Text outperforms eight mainstream instruction-tuning corpora on GaokaoBench and EduEval across two base models [+M]
- SFT on K12-Train improves out-of-domain subjects (Chinese, Humanities Math), suggesting acquisition of a transferable structurally-grounded answer style [+M]
Related Products and Programmes¶
Key Sources¶
- Hao Liang, Qihan Lin, Mingrui Chen, Hengyi Feng, Zhaoyang Han, Xiaochen Ma, Zhen Hao Wong, Meiyi Qiang, Linzhuang Sun, Wentao Zhang. (2026). K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training Educational LLMs. arXiv preprint. https://arxiv.org/abs/2605.09635
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
k12-train |
title |
K12-Train |
description |
K12-Train is a knowledge-graph-guided dataset and generation pipeline by Hao Liang et al. |
status |
review |
product_kind |
dataset |
generated |
by process:settle-candidates on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
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