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cs.CL2026
A Systematic Analysis of the Predictive Power of LM Surprisal in Reading Chinese
Hongao Zhu, Muxiaoqiao Xu, Yikang Liu +4
This study analyzes the predictive power of LM-derived, token-level surprisal on Mandarin Chinese reading times. Using a suite of Chinese-Pythia models (14M--1.4B) trained from scr…
cs.CL2026
The First ChineseBabyLM Challenge: training data-efficient and cognitively plausible language models for Chinese
Siyuan Song, Zhiheng Qian, Yunhao Zhang +11
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 10…
cs.CL2026
LLMs for automatic annotation of Mandarin narrative transcripts
Qingwen Zhao, Hongao Zhu, Yunqi He +3
Linguistic annotation of transcribed speech is essential for research in language acquisition, language disorders, and sociolinguistics, yet remains labor-intensive and time-consum…