Publications

(*=equal contribution, #=corresponding) Check the full list on Google Scholar

Preprints

• Qizhi Pei, Lijun Wu#, Kaiyuan Gao, Jinhua Zhu, Yue Wang, Zun Wang, Tao Qin, Rui Yan, Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey, 2024.
• Microsoft Research AI4Science, Microsoft Azure Quantum, The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4, 2023.
• Hany Hassan, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, Christian Federmann, Xuedong Huang, Marcin Junczys-Dowmunt, William Lewis, Mu Li, Shujie Liu, Tie-Yan Liu, Renqian Luo, Arul Menezes, Tao Qin, Frank Seide, Xu Tan, Fei Tian, Lijun Wu, Shuangzhi Wu, Yingce Xia, Dongdong Zhang, Zhirui Zhang and Ming Zhou, Achieving Human Parity on Automatic Chinese to English News Translation, 2018 (The first Chinese-to-English machine translation system that can match the human translation accuracy！).

Journals

• Kehan Wu, Yingce Xia, Pan Deng, Renhe Liu, Yuan Zhang, Han Guo, Yumeng Cui, Qizhi Pei, Lijun Wu, Shufang Xie, Si Chen, Xi Lu, Song Hu, Jinzhi Wu, Chi-Kin Chan, Shawn Chen, Liangliang Zhou, Nenghai Yu, Enhong Chen, Haiguang Liu, Jinjiang Guo, Tao Qin, Tie-Yan Liu, Target-aware Molecule Generation for Drug Design Using a Chemical Language Model, In Nature Communications, 2024.
• Juntao Li, Xiaobo Liang, Lijun Wu#, Yue Wang, Qi Meng, Tao Qin, Min Zhang, Tie-Yan Liu, Randomness Regularization with Simple Consistency Training for Neural Networks, In IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI-2024).
• Qizhi Pei, Lijun Wu#, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Haiguang Liu, Tie-Yan Liu, SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction, In Briefings in Bioinformatics-2023. [code]
• Xiaobo Liang, Runze Mao, Lijun Wu#, Juntao Li, Min Zhang, Qing Li, Enhancing Low-Resource NLP by Consistency Training with Data and Model Permutations, In IEEE/ACM Transactions on Audio, Speech and Language Processing (IEEE TASLP-2023).
• Yue Wang, Lijun Wu, Juntao Li, Xiaobo Liang, Min Zhang, Are the BERT Family Zero-Shot Learners? A Study on Their Potential and Limitations, In Artificial Intelligence (2023).
• Yisheng Xiao*, Lijun Wu*, Junliang Guo, Juntao Li, Min Zhang, Tao Qin, and Tie-Yan Liu, A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond (A comprehensive survey for Non-autoregressive generation), IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI-2023). [code]
• Jiacheng Lin, Lijun Wu#, Jinhua Zhu, Xiaobo Liang, Yingce Xia, Shufang Xie, Tao Qin, Tie-Yan Liu, R$^2$-DDI: Relation-aware Feature Refinement for Drug-Drug Interaction Prediction, In Briefings in Bioinformatics-2022. [code]
• Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Yusong Wang, Tong Wang, Wengang Zhou, Tao Qin, Houqiang Li, Haiguang Liu, Tie-Yan Liu, Direct Molecular Conformation Generation, In Transactions on Machine Learning Research (TMLR-2022). [code]
• Yutai Hou, Yingce Xia, Lijun Wu, Shufang Xie, Yang Fan, Jinhua Zhu, Wanxiang Che, Tao Qin, Tie-Yan Liu, Discovering Drug-Target Interaction Knowledge from Biomedical Literature, In Bioinformatics-2022.
• Lijun Wu#, Chengcan Yin, Jinhua Zhu, Zhen Wu, Liang He,Yingce Xia, Shufang Xie, Tao Qin, Tie-Yan Liu, SPRoBERTa: Protein Embedding Learning with Local Fragment Modeling, In Briefings in Bioinformatics-2022.
• Jinhua Zhu, Yingce Xia, Lijun Wu, Jiajun Deng, Wengang Zhou, Tao Qin, Tie-Yan Liu, and Houqiang Li, Masked Contrastive Representation Learning for Reinforcement Learning, In IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI-2022). [code]
• Bo Yang, Lijun Wu#, Jinhua Zhu, Bo Shao, Xiaola Lin, Tie-Yan Liu, Multimodal Sentiment Analysis with Two-Phase Multi-Task Learning, In IEEE/ACM Transactions on Audio, Speech and Language Processing (IEEE/ACM TASLP-2022). [code]
• Xiaobo Liang*, Lijun Wu*, Juntao Li, Tao Qin, Min Zhang, Tie-Yan Liu, Multi-Teacher Distillation with Single Model for Neural Machine Translation, In IEEE/ACM Transactions on Audio, Speech and Language Processing (IEEE/ACM TASLP-2022). [code]
• Bo Yang, Bo Shao, Lijun Wu#, Xiaola Lin, Multimodal Sentiment Analysis with Unidirectional Modality Translation, In Neurocomputing (Neurocomputing-2022).
• Yang Fan, Yingce Xia, Jinhua Zhu, Lijun Wu, Shufang Xie, Tao Qin, Back Translation for Molecule Generation, In Bioinformatics-2021. [code]
• Lijun Wu, Xu Tan, Tao Qin, Jianhuang Lai and Tie-Yan Liu, Beyond Error Propagation: Language Branching Also Affects the Accuracy of Sequence Generation, In IEEE/ACM Transactions on Audio, Speech and Language Processing (IEEE/ACM TASLP-2019).

Conferences

• Yangzhe Peng, Kaiyuan Gao, Liang He, Yuheng Cong, Haiguang Liu, Kun He, Lijun Wu#, CovDocker: Benchmarking Covalent Drug Design with Tasks, Datasets, and Solutions, In the 31st SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2025).
• Yu Li, Qizhi Pei, Mengyuan Sun, Honglin Lin, Chenlin Ming, Xin Gao, Jiang Wu, Conghui He, Lijun Wu#, Cipherbank: Exploring the boundary of llm reasoning capabilities through cryptography challenges, In 63rd Annual Meeting of the Association for Computational Linguistics (ACL-2025).
• Xin Gao, Qizhi Pei, Zinan Tang, Yu Li, Honglin Lin, Jiang Wu, Conghui He, Lijun Wu#, A Strategic Coordination Framework of Small LLMs Matches Large LLMs in Data Synthesis, In 63rd Annual Meeting of the Association for Computational Linguistics (ACL-2025).
• Zhuoshi Pan, Yu Li, Honglin Lin, Qizhi Pei, Zinan Tang, Wei Wu, Chenlin Ming, H Vicky Zhao, Conghui He, Lijun Wu#, Lemma: Learning from errors for mathematical advancement in llms, In 63rd Annual Meeting of the Association for Computational Linguistics (ACL-2025).
• Qizhi Pei, Lijun Wu#, Zhuoshi Pan, Yu Li, Honglin Lin, Chenlin Ming, Xin Gao, Conghui He, Rui Yan, Mathfusion: Enhancing mathematic problem-solving of llm through instruction fusion, In 63rd Annual Meeting of the Association for Computational Linguistics (ACL-2025).
• Qizhi Pei, Lijun Wu#, Kaiyuan Gao, Jinhua Zhu, Rui Yan, 3D-MolT5: Towards Unified 3D Molecule-Text Modeling with 3D Molecular Tokenization, In the Thirteenth International Conference on Learning Representations (ICLR-2025).
• Zizhuo Zhang, Lijun Wu#, Kaiyuan Gao, Jiangchao Yao, Tao Qin, Bo Han, Fast and Accurate Blind Flexible Docking, In the Thirteenth International Conference on Learning Representations (ICLR-2025).
• Zun Wang, Chang Liu, Nianlong Zou, He Zhang, Xinran Wei, Lin Huang, Lijun Wu, Bin Shao, Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models, In The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeruIPS-2024).
• Lei Shen, Zhenheng Tang, Lijun Wu, Yonggang Zhang, Xiaowen Chu, Tao Qin, Bo Han, Hot Pluggable Federated Learning, In International Workshop on Federated Foundation Models In Conjunction with NeurIPS 2024 (FL@FM-NeurIPS2024) (Outstanding Student Paper Award, Oral).
• Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, Sheng Wang, Lingpeng Kong, Retrieved Sequence Augmentation for Protein Representation Learning, In 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP-2024).
• Qizhi Pei, Lijun Wu#, Kaiyuan Gao, Jinhua Zhu, Rui Yan, Enhanced BioT5+ for Molecule-Text Translation: A Three-Stage Approach with Data Distillation, Diverse Training, and Voting Ensemble, In The 1st Workshop on Language + Molecules (Language+Molecules@ ACL-2024) (Oral, 1st/2nd winner solution for sharing tasks).
• QiZhi Pei*, Lijun Wu#*, Zhenyu He, Jinhua Zhu, Yingce Xia, Shufang Xie, Rui Yan, Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest Neighbors, In 33rd ACM International Conference on Information and Knowledge Management (CIKM-2024).
• Qizhi Pei, Lijun Wu#, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, Rui Yan, BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning, In 62nd Annual Meeting of the Association for Computational Linguistics (ACL-2024 Findings).
• Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu#, Yingce Xia, Rui Yan, BioT5: Enriching Cross-model Integration in Biology with Chemical Knowledge and Natural Language Associations, In 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP-2023). [code]
• Qizhi Pei, Kaiyuan Gao, Lijun Wu#, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan, FABind: Fast and Accurate Protein-Ligand Binding, In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS-2023). [code] [project]
• Kaiyuan Gao, Lijun Wu#, Jinhua Zhu, Tianbo Peng, Yingce Xia, Liang He, Shufang Xie, Tao Qin, Haiguang Liu, Kun He and Tie-Yan Liu, Pre-training Antibody Language Models for Antigen-Specific Computational Antibody Design, In the 29th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2023). [code]
• Jinhua Zhu, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li and Tie-Yan Liu, Dual-view molecule pre-training, In the 29th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2023). [code]
• Chong Liu, Xiaoyang Liu, Rongqin Zheng, Lixin Zhang, Xiaobo Liang, Juntao Li, Lijun Wu, Min Zhang, Leyu Lin, CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation, In the 29th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2023) (CT4Rec has been shaped into Tencent News Recommendation!). [code]
• Xiaobo Liang, Juntao Li, Lijun Wu, Ziqiang Cao and Min Zhang, Dynamic and Efficient Inference for Text Generation via BERT Family, In 61st Annual Meeting of the Association for Computational Linguistics (ACL-2023). [code]
• Zequn Liu, Wei Zhang, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Ming Zhang and Tie-Yan Liu, MolXPT: Wrapping Molecules with Text for Generative Pre-training, In 61st Annual Meeting of the Association for Computational Linguistics (ACL-2023).
• Zijie Geng, Shufang Xie, Yingce Xia, Lijun Wu, Tao Qin, Jie Wang, Yongdong Zhang, Feng Wu and Tie-Yan Liu, De Novo Molecular Generation via Connection-aware Motif Mining, In the Eleventh International Conference on Learning Representations (ICLR-2023).
• Jinhua Zhu, Yue Wang, Lijun Wu, Tao Qin, Wengang Zhou, Tie-Yan Liu and Houqiang Li, Making Better Decision by Directly Planning in Continuous Control, In the Eleventh International Conference on Learning Representations (ICLR-2023). [code]
• Jinhua Zhu, Kehan Wu, Bohan Wang, Yingce Xia, Shufang Xie, Qi Meng, Lijun Wu, Tao Qin, Wengang Zhou, Houqiang Li and Tie-Yan Liu, O-GNN: incorporating ring priors into molecular modeling, In the Eleventh International Conference on Learning Representations (ICLR-2023).
• Yisheng Xiao, Lijun Wu, Ruiyang Xu, Juntao Li, Tao Qin, Tie-yan Liu, Min Zhang, AMOM: Adaptive Masking over Masking for Conditional Masked Language Model, In Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-2023).
• Shufang Xie, Rui Yan, Junliang Guo, Yingce Xia, Lijun Wu, Tao Qin, Retrosynthesis Prediction with Local Template Retrieval, In Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-2023).
• Xiaobo Liang, Juntao Li, Lijun Wu, Min Zhang, JANUS: Joint Autoregressive and Non-autoregressive Training with Auxiliary Loss for Sequence Generation, In 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP-2022). [code]
• Zhaochen Su, Zecheng Tang, Xinyan Guan, Juntao Li, Lijun Wu, Min Zhang, Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change, In 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP-2022).
• Jinhua Zhu, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li and Tie-Yan Liu. Unified 2D and 3D Pre-Training of Molecular Representations, In the 28th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2022). [code]
• Shufang Xie, Peng Han, Yingce Xia, Lijun Wu, Tao Qin, Chenjuan Guo, Bin Yang and Rui Yan. RetroGraph: Retrosynthetic Planning with Graph Search, In the 28th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2022).
• Shufang Xie, Ang Lv, Yingce Xia, Lijun Wu, Tao Qin, Rui Yan, Tie-Yan Liu, Target-Side Data Augmentation for Sequence Generation, In The Tenth International Conference on Learning Representations (ICLR-2022). [code]
• Xiaobo Liang*, Lijun Wu*, Juntao Li, Yue Wang, Qi Meng, Tao Qin, Wei Chen, Min Zhang, Tie-Yan Liu, R-Drop: Regularized Dropout for Neural Networks, In Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS-2021) (R-Drop has been shaped into Microsoft Translator and helped improve more than 20+ language translations!). [code]
• Weijiang Yu, Haoteng Zheng, Mengfei Li, Lei Ji, Lijun Wu, Nong Xiao, Nan Duan, Learning from Inside: Self-driven Siamese Sampling and Reasoning for Video Question Answering?, In Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS-2021).
• Bo Yang, Lijun Wu#, How to Leverage Multimodal EHR Data for Better Medical Predictions?, In 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP-2021). [code]
• Xueqing Wu, Lewen Wang, Yingce Xia, Weiqing Liu, Lijun Wu, Shufang Xie, Tao Qin and Tie-Yan Liu, Temporally Correlated Task Scheduling for Sequence Learning, In Thirty-eighth International Conference on Machine Learning (ICML-2021).
• Boning Li, Yingce Xia, Shufang Xie, Lijun Wu and Tao Qin, Distance-Enhanced Graph Neural Network for Link Prediction, In The 2021 ICML Workshop on Computational Biology (ICML-2021 workshop).
• Zhen Wu, Lijun Wu#, Qi Meng, Yingce Xia, Shufang Xie, Tao Qin, Xinyu Dai and Tie-Yan Liu, UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost, In The 2021 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL-2021).
• Jinhua Zhu*, Lijun Wu*, Yingce Xia, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu, IOT: Instance-wise Layer Reordering for Transformer Structures, In The Ninth International Conference on Learning Representations (ICLR-2021). [code]
• Xueqing Wu, Yingce Xia, Jinhua Zhu, Lijun Wu, Shufang Xie and Tao Qin, mixSeq: A Simple Data Augmentation Method for Neural Machine Translation, In International Conference on Spoken Language Translation (IWSLT-2021).
• Yang Fan, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Jiang Bian, Xiangyang Li, Tao Qin, Learning to Reweight with Deep Interactions, In Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-2021).
• Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, Jian Yang, Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection, In 34th Conference on Neural Information Processing System (NeurIPS-2020). [code]
• Lijun Wu, Shufang Xie, Yingce Xia, Yang Fan, Tao Qin, Jianhuang Lai, Tie-Yan Liu, Sequence Generation with Mixed Representations, In Thirty-seventh International Conference on Machine Learning (ICML-2020). [code]
• Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu, Incorporating BERT into Neural Machine Translation, In Eighth International Conference on Learning Representations (ICLR-2020). [code]
• Yiren Wang*, Lijun Wu*, Yingce Xia, Tao Qin, Chengxiang Zhai, Tie-Yan Liu, Transductive Ensemble Learning for Neural Machine Translation, In Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020).
• Lijun Wu*, Yiren Wang*, Yingce Xia, Tao Qin, Jianhuang Lai, and Tie-Yan Liu, Exploiting Monolingual Data at Scale for Neural Machine Translation, In 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP-2019) (Our technique helped won the WMT-19 champion in multiple language translations!).
• Lijun Wu, Jinhua Zhu, Fei Gao, Di He, Tao Qin, Jianhuang Lai, and Tie-Yan Liu, Machine Translation with Weakly Paired Documents, In 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP-2019).
• Lijun Wu*, Yiren Wang*, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, Jianhuang Lai, and Tie-Yan Liu, Depth Growing for Neural Machine Translation, In 57th Annual Meeting of the Association for Computational Linguistics (ACL-2019). [code]
• Jinhua Zhu*, Fei Gao*, Lijun Wu, Yingce Xia, Tao Qin, Wengang Zhou, Xueqi Cheng, and Tie-Yan Liu, Soft Contextual Data Augmentation for Neural Machine Translation, In 57th Annual Meeting of the Association for Computational Linguistics (ACL-2019). [code]
• Yingce Xia, Xu Tan, Fei Tian, Fei Gao, Weicong Chen, Yang Fan, Linyuan Gong, Yichong Leng, Renqian Luo, Yiren Wang, Lijun Wu, Jinhua Zhu, Tao Qin, and Tie-Yan Liu, Microsoft Research Asia’s Systems for WMT19, In Proceedings of the Fourth Conference on Machine Translation (WMT-2019) (Champion winner solution for WMT-19!).
• Lijun Wu*, Fei Tian*, Yingce Xia, Yang Fan, Tao Qin, Jianhuang Lai and Tie-Yan Liu, Learning to Teach with Dynamic Loss Functions, In 32th Conference on Neural Information Processing Systems (NeurIPS-2018). [code]
• Lijun Wu, Fei Tian, Tao Qin, Jianhuang Lai and Tie-Yan Liu, A Study of Reinforcement Learning for Neural Machine Translation, In 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP-2018) (Oral). [code]
• Lijun Wu*, Xu Tan*, Di He, Fei Tian, Tao Qin, Jianhuang Lai and Tie-Yan Liu, Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter, In 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP-2018) (Oral).
• Lijun Wu, Fei Tian, Li Zhao, Jianhuang Lai and Tie-Yan Liu, Word Attention for Sequence to Sequence Text Understanding, In Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-2018). [code]
• Lijun Wu, Yingce Xia, Fei Tian, Li Zhao, Tao Qin, Jianhuang Lai and Tie-Yan Liu, Adversarial Neural Machine Translation, In 10th Asian Conference on Machine Learning (ACML-2018) (Most cited paper in ACML).
• Fei Gao, Lijun Wu, Li Zhao, Tao Qin, Xueqi Cheng and Tie-Yan Liu, Efficient Sequence Learning with Group Recurrent Networks, In 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-2018).
• Lijun Wu, Li Zhao, Tao Qin, Jianhuang Lai and Tie-Yan Liu, Sequence Prediction with Unlabeled Data by Reward Function Learning, In 26th International Joint Conference on Artifcial Intelligence (IJCAI-2017).
• Yingce Xia, Fei Tian, Lijun Wu, Jianxin Lin, Tao Qin, Nenghai Yu and Tie-Yan Liu, Deliberation Networks: Sequence Generation Beyond One-Pass Decoding, In 31st Conference on Neural Information Processing Systems (NIPS-2017).