Publications

2023

  1. Binding Language Models in Symbolic Languages
    Zhoujun Cheng, Tianbao Xie, Peng Shi, Chengzu Li, Rahul Nadkarni, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, Noah A Smith, and Tao Yu,
    In International Conference on Learning Representations (ICLR 2023) 2023
    arXiv Code Poster
  2. Selective Annotation Makes Language Models Better Few-Shot Learners
    Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu,
    In International Conference on Learning Representations (ICLR 2023) 2023
    arXiv Code
  3. Efficient Attention via Control Variates
    Lin Zheng, Jianbo Yuan, Chong Wang, and Lingpeng Kong,
    In International Conference on Learning Representations (ICLR 2023) 2023
    arXiv Code
  4. Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning
    Jiahui Gao, Renjie Pi, Lin Yong, Hang Xu, Jiacheng Ye, Zhiyong Wu, Weizhong Zhang, Xiaodan Liang, Zhenguo Li, and Lingpeng Kong,
    In International Conference on Learning Representations (ICLR 2023) 2023
    arXiv Code
  5. DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models
    Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and Lingpeng Kong,
    In International Conference on Learning Representations (ICLR 2023) 2023
    arXiv Code
  6. Unsupervised Explanation Generation via Correct Instantiations
    Sijie Chen, Zhiyong Wu, Jiangjie Chen, Zhixing Li, Yang Liu, and Lingpeng Kong,
    In Proceedings of AAAI Conference on Artificial Intelligence (AAAI 2023) 2023
    arXiv Code
  7. Retrieved Sequence Augmentation for Protein Representation Learning
    Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, and Lingpeng Kong,
    arXiv preprint arXiv:2302.12563 2023
    arXiv Code
  8. A Reparameterized Discrete Diffusion Model for Text Generation
    Lin Zheng, Jianbo Yuan, Lei Yu, and Lingpeng Kong,
    arXiv preprint arXiv:2302.04542 2023
    arXiv Code
  9. Compositional Exemplars for In-context Learning
    Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Tao Yu, and Lingpeng Kong,
    In Proceedings of the International Conference on Machine Learning (ICML 2023) 2023
    arXiv Code
  10. CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling
    Jun Zhang, Shuyang Jiang, Jiangtao Feng, Lin Zheng, and Lingpeng Kong,
    In Proceedings of the International Conference on Machine Learning (ICML 2023) 2023
    arXiv Code

2022

  1. One Embedder, Any Task: Instruction-Finetuned Text Embeddings
    Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A Smith, Luke Zettlemoyer, and Tao Yu,
    Preprint 2022
    arXiv Code Poster
  2. DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation
    Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Scott Wen-tau Yih, Daniel Fried, Sida Wang, and Tao Yu,
    Preprint 2022
    arXiv Code Poster
  3. Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play
    Qi Liu, Zihuiwen Ye, Tao Yu, Phil Blunsom, and Linfeng Song,
    In Findings of EMNLP 2022 2022
    arXiv Code
  4. Evaluating Self-Supervised Learning for Molecular Graph Embeddings
    Hanchen Wang, Jean Kaddour, Shengchao Liu, Jian Tang, Matt Kusner, Joan Lasenby, and Qi Liu,
    arXiv preprint arXiv:2206.08005 2022
    arXiv
  5. Retrieval-enhanced Graph Neural Networks for Graph Property Prediction
    Dingmin Wang, Shengchao Liu, Hanchen Wang, Linfeng Song, Jian Tang, Song Le, Bernardo Cuenca Grau, and Qi Liu,
    arXiv preprint arXiv:2206.00362 2022
    arXiv
  6. ZeroGen: Efficient Zero-shot Learning via Dataset Generation
    Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, and Lingpeng Kong,
    In Empirical Methods in Natural Language Processing (EMNLP 2022) 2022
    arXiv Code
  7. UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
    Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida Wang, Victor Zhong,  Bailin Wang, and 11 more authors
    In Empirical Methods in Natural Language Processing (EMNLP 2022) 2022
    arXiv Code Poster
  8. Linear Complexity Randomized Self-attention Mechanism
    Lin Zheng, Chong Wang, and Lingpeng Kong,
    In Proceedings of the International Conference on Machine Learning (ICML 2022) 2022
    arXiv Code
  9. Ripple Attention for Visual Perception with Sub-quadratic Complexity
    Lin Zheng, Huijie Pan, and Lingpeng Kong,
    In Proceedings of the International Conference on Machine Learning (ICML 2022) 2022
    arXiv
  10. Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling
    Jakob Prange, Nathan Schneider, and Lingpeng Kong,
    In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2022) 2022
    arXiv Code
  11. Event Transition Planning for Open-ended Text Generation
    Qintong Li, Piji Li, Wei Bi, Zhaochun Ren, Yuxuan Lai, and Lingpeng Kong,
    In Findings of the Annual Meeting of the Association for Computational Linguistics (ACL 2022 Findings) 2022
    arXiv Code
  12. Lexical Knowledge Internalization for Neural Dialog Generation
    Zhiyong Wu, Wei Bi, Xiang Li, Lingpeng Kong, and Ben Kao,
    In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2022) 2022
    arXiv Code
  13. ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback
    Jiacheng Ye, Jiahui Gao, Zhiyong Wu, Jiangtao Feng, Tao Yu, and Lingpeng Kong,
    In Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2022 Findings) 2022
    arXiv Code
  14. Self-adaptive In-context Learning
    Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, and Lingpeng Kong,
    arXiv preprint arXiv:2212.10375 2022
    arXiv Code

2021

  1. Cascaded Head-colliding Attention
    Lin Zheng, Zhiyong Wu, and Lingpeng Kong,
    In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2021) 2021
    arXiv Code
  2. Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation
    Zhiyong Wu, Lingpeng Kong, Wei Bi, Xiang Li, and Ben Kao,
    In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2021) 2021
    arXiv Code