Shaolin Zhu(朱少林)

Associate Professor
Tianjin University (天津大学)
Department of Computer Science
Office: 55-B115
zhushaolin@tju.edu.cn

I am a tenure-track Associate Professor in Department of Computer Science at Tianjin University, and a member of TJUNLP Lab, led by Prof. Deyi Xiong. I received my Ph.D. degree from University of Chinese Academic of Science. My research interests include Natural Language Processing, Multimodal Processing, Machine Translation. I am looking for self-motivated students interested in building systems for large language model and deep neural network. Feel free to zhushaolin@tju.edu.cn if you want to join us!

Selected Publications

• [Information Processing & Management, 2025] Overcoming language barriers via machine translation with sparse Mixture-of-Experts fusion of large language models (中科院一区)[一作]
• [AAAI 2025] LRM-LLaVA: Overcoming the Modality Gap of Multilingual Large Language-Vision Model for Low-Resource Languages (CCF-A)[通讯作者]
• [Knowledge-based Systems 2025] CoLE: Chain of Legal Experts Prompting for Large Language Models in Law (中科院一区)[通讯作者]
• [COLING 2024] Towards Robust In-Context Learning for Machine Translation with Large Language Models (CCF-B, Outstanding Paper)[一作]
• [ACL 2024] LANDeRMT: Dectecting and Routing Language-Aware Neurons for Selectively Finetuning LLMs to Machine Translation (CCF-A)[一作]
• [Tsinghua Science and Technology, 2024] Enhancing Aspect-based Sentiment Analysis in Tourism using Large Language Models and Positional Information (中科院一区)[通讯作者]
• [Information Processing & Management, 2024] FEDS-ICL: Enhancing translation ability and efficiency of large language model by optimizing demonstration selection (中科院一区)[一作]
• [Expert Systems with Applications, 2024] Vision-Guided Target-Side Future Context Learning for Multimodal Machine Translation (中科院一区)[一作]
• [ACL 2023] PEIT: Bridging the Modality Gap with Pre-trained Models for End-to-End Image Translation (CCF-A)[一作]
• [Knowledge and information systems, 2023] Mining Parallel Sentences from Internet with Multi-view Knowledge Distillation for Low-resource Language Pairs (CCF-B)[一作]