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arXiv · 2607.10745

The First ChineseBabyLM Challenge: training data-efficient and cognitively plausible language models for Chinese

Siyuan Song·Zhiheng Qian·Yunhao Zhang·Linyang He·Xiaozhe Ji·Yingxin Lin·Hongao Zhu·Chongtian Shao·Chuhan Lang·Luan Li·Rui Wang·Renfen Hu·Shaonan Wang·Hai Hu
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Abstract

This paper presents the first ChineseBabyLM Challenge, organized as part of NLPCC 2026. The challenge asked participants to train language models from scratch using no more than 102M Chinese words. The models were evaluated on three tracks: natural language understanding, cognitive alignment, and Hanzi knowledge. There were no restrictions on tokenizers, model architectures, or the number of training epochs. Eighteen teams submitted 28 distinct models, generating 74 result files. The overall-winning team used a DeBERTa-v2 architecture and introduced an auxiliary pinyin-prediction objective during pretraining. Several submissions also explored curriculum-learning strategies and architectural innovations. Overall, the challenge provides a benchmark for advancing data-efficient and cognitively plausible approaches to Chinese language modeling.

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Siyuan Song, Zhiheng Qian, Yunhao Zhang, Linyang He, Xiaozhe Ji, Yingxin Lin, Hongao Zhu, Chongtian Shao, Chuhan Lang, Luan Li, Rui Wang, Renfen Hu, Shaonan Wang, Hai Hu. 2026-07-12. The First ChineseBabyLM Challenge: training data-efficient and cognitively plausible language models for Chinese. https://arxiv.org/abs/2607.10745

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