We're actively applying the techniques of machine translation and natural language processing in interdisciplinary research. For example, to analyze ancient Chinese, to generate or translate ancient Chinese, to understand the language comprehension process of human brain, to modeling the structure of protein, etc.
A More Complete List on Google Scholar
* marks corresponding author(s).
2025
Elucidating the Design Space of Multimodal Protein Language Models. (DPLM-2.1)
Cheng-Yen Hsieh, Xinyou Wang, Daiheng Zhang, Dongyu Xue, Fei Ye, Shujian Huang, Zaixiang Zheng, Quanquan Gu*.
ICML 2025. (arxiv:2504.11454)
DPLM-2: A Multimodal Diffusion Protein Language Model.
Xinyou Wang, Zaixiang Zheng, Fei YE, Dongyu Xue, Shujian Huang, Quanquan Gu*.
ICLR 2025. (arxiv:2410.13782)
2024
Diffusion Language Models Are Versatile Protein Learners.
Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu*.
ICML 2024. (arxiv:2402.18567)
Measuring Meaning Composition in the Human Brain with Composition Scores from Large Language Models.
Changjiang Gao, Jixing Li*, Jiajun Chen, Shujian Huang*.
ACL 2024. (arxiv:2403.04325, code)
2023
Dictionary Definition Augemented Neural Machine Translation for Anciet Chinese Text.
Jiahuan Li, Ruochun Wu, Wenjing Hu, Jixuan Chen, Weilu Xu, Shujian Huang*, Jiajun Chen.
CCMT2023 (in Chinese). Best Paper Award.
Roles of Scaling and Instruction Tuning in Language Perception: Model vs. Human Attention.
Changjiang Gao, Shujian Huang*, Jixing Li, Jiajun Chen.
Findings of EMNLP 2023.