6 papers · 1 filter
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…
Decoding the Multimodal Mind: Generalizable Brain-to-Text Translation via Multimodal Alignment and Adaptive Routing
Chunyu Ye, Yunhao Zhang, Jingyuan Sun +3
Decoding language from the human brain remains a grand challenge for Brain-Computer Interfaces (BCIs). Current approaches typically rely on unimodal brain representations, neglecti…
Discovering Semantic Subdimensions through Disentangled Conceptual Representations
Yunhao Zhang, Shaonan Wang, Nan Lin +3
Understanding the core dimensions of conceptual semantics is fundamental to uncovering how meaning is organized in language and the brain. Existing approaches often rely on predefi…
Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual Alignment
Chong Li, Shaonan Wang, Jiajun Zhang +1
Multilingual generative models obtain remarkable cross-lingual in-context learning capabilities through pre-training on large-scale corpora. However, they still exhibit a performan…
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions
Chong Li, Wen Yang, Jiajun Zhang +3
Large language models respond well in high-resource languages like English but struggle in low-resource languages. It may arise from the lack of high-quality instruction following…
Navigating Brain Language Representations: A Comparative Analysis of Neural Language Models and Psychologically Plausible Models
Yunhao Zhang, Shaonan Wang, Xinyi Dong +2
Neural language models, particularly large-scale ones, have been consistently proven to be most effective in predicting brain neural activity across a range of studies. However, pr…