10 citations · 13 across the 11 of their papers we have counts for
12 papers
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…
Computational Lesions in Multilingual Language Models Separate Shared and Language-specific Brain Alignment
Yang Cui, Jingyuan Sun, Yizheng Sun +8
How the brain supports language across different languages is a basic question in neuroscience and a useful test for multilingual artificial intelligence. Neuroimaging has identifi…
Component-Level Lesioning of Language Models Reveals Clinically Aligned Aphasia Phenotypes
Yifan Wang, Jichen Zheng, Jingyuan Sun +5
Large language models (LLMs) increasingly exhibit human-like linguistic behaviors and internal representations that they could serve as computational simulators of language cogniti…
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…
Bridging Brains and Models: MoE-Based Functional Lesions for Simulating and Rehabilitating Aphasia
Yifan Wang, Jingyuan Sun, Jichen Zheng +5
The striking alignment between large language models (LLMs) and human brain activity positions them as powerful models of healthy cognition. This parallel raises a fundamental ques…
How Syntax Specialization Emerges in Language Models
Xufeng Duan, Zhaoqian Yao, Yunhao Zhang +2
Large language models (LLMs) have been found to develop surprising internal specializations: Individual neurons, attention heads, and circuits become selectively sensitive to synta…
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…
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…
Computational Models to Study Language Processing in the Human Brain: A Survey
Shaonan Wang, Jingyuan Sun, Yunhao Zhang +3
Despite differing from the human language processing mechanism in implementation and algorithms, current language models demonstrate remarkable human-like or surpassing language ca…
MulCogBench: A Multi-modal Cognitive Benchmark Dataset for Evaluating Chinese and English Computational Language Models
Yunhao Zhang, Xiaohan Zhang, Chong Li +2
Pre-trained computational language models have recently made remarkable progress in harnessing the language abilities which were considered unique to humans. Their success has rais…
Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning
Chong Li, Shaonan Wang, Yunhao Zhang +2
Transformer-based models, even though achieving super-human performance on several downstream tasks, are often regarded as a black box and used as a whole. It is still unclear what…
Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activities
Jingyuan Sun, Mingxiao Li, Zijiao Chen +3
Decoding visual stimuli from neural responses recorded by functional Magnetic Resonance Imaging (fMRI) presents an intriguing intersection between cognitive neuroscience and machin…