6 citations · 28 across the 29 of their papers we have counts for
30 papers
TokAlign++: Advancing Vocabulary Adaptation via Better Token Alignment
Chong Li, Yingzhuo Deng, Wen Yang +2
Tokenization is a foundational step in the text process of Large Language Models (LLMs). Texts must be first tokenized into token IDs, which are then input to LLMs. Inefficient tok…
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…
How Well Does Mathematical Reasoning Transfer Across Languages in English-Centric LLMs?
Wen Yang, Junhong Wu, Chong Li +2
Recent advances in Reinforcement Post-Training (RPT) have substantially improved the mathematical reasoning capabilities of large language models (LLMs), but it remains unclear how…
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…
OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model
Chen Wang, Tianyu Peng, Wen Yang +8
Empathetic interaction is a cornerstone of human-machine communication, due to the need for understanding speech enriched with paralinguistic cues and generating emotional and expr…
Group then Scale: Dynamic Mixture-of-Experts Multilingual Language Model
Chong Li, Yingzhuo Deng, Jiajun Zhang +1
The curse of multilinguality phenomenon is a fundamental problem of multilingual Large Language Models (LLMs), where the competition between massive languages results in inferior p…
TokAlign: Efficient Vocabulary Adaptation via Token Alignment
Chong Li, Jiajun Zhang, Chengqing Zong
Tokenization serves as a foundational step for Large Language Models (LLMs) to process text. In new domains or languages, the inefficiency of the tokenizer will slow down the train…
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…
Implicit Cross-Lingual Rewarding for Efficient Multilingual Preference Alignment
Wen Yang, Junhong Wu, Chen Wang +2
Direct Preference Optimization (DPO) has become a prominent method for aligning Large Language Models (LLMs) with human preferences. While DPO has enabled significant progress in a…
ChineseWebText 2.0: Large-Scale High-quality Chinese Web Text with Multi-dimensional and fine-grained information
Wanyue Zhang, Ziyong Li, Wen Yang +5
During the development of large language models (LLMs), pre-training data play a critical role in shaping LLMs' capabilities. In recent years several large-scale and high-quality p…
Language Imbalance Driven Rewarding for Multilingual Self-improving
Wen Yang, Junhong Wu, Chen Wang +2
Large Language Models (LLMs) have achieved state-of-the-art performance across numerous tasks. However, these advancements have predominantly benefited "first-class" languages such…
BLSP-Emo: Towards Empathetic Large Speech-Language Models
Chen Wang, Minpeng Liao, Zhongqiang Huang +3
The recent release of GPT-4o showcased the potential of end-to-end multimodal models, not just in terms of low latency but also in their ability to understand and generate expressi…
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…
MapGuide: A Simple yet Effective Method to Reconstruct Continuous Language from Brain Activities
Xinpei Zhao, Jingyuan Sun, Shaonan Wang +3
Decoding continuous language from brain activity is a formidable yet promising field of research. It is particularly significant for aiding people with speech disabilities to commu…
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…
MoDS: Model-oriented Data Selection for Instruction Tuning
Qianlong Du, Chengqing Zong, Jiajun Zhang
Instruction tuning has become the de facto method to equip large language models (LLMs) with the ability of following user instructions. Usually, hundreds of thousands or millions…
ChineseWebText: Large-scale High-quality Chinese Web Text Extracted with Effective Evaluation Model
Jianghao Chen, Pu Jian, Tengxiao Xi +7
During the development of large language models (LLMs), the scale and quality of the pre-training data play a crucial role in shaping LLMs' capabilities. To accelerate the research…
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…
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…
BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing
Chen Wang, Minpeng Liao, Zhongqiang Huang +5
The emergence of large language models (LLMs) has sparked significant interest in extending their remarkable language capabilities to speech. However, modality alignment between sp…