Zhengbao Jiang (江政宝)

I am a PhD student at Language Technologies Institute of Carnegie Mellon University. I am fortunate to be advised by Graham Neubig, working on Natural Language Processing, Information Retrieval, and Machine Learning. I focus on addressing knowledge-intensive tasks such as QA and reasoning through either nonparametric methods (i.e., retrieval-augmented generation) or parametric methods (i.e., knowledge updates on LLMs followed by close-book prompting).

I received my Bachelor’s and Master’s degree in Computer Science from Renmin University of China in 2015 and 2018, where I worked with Zhicheng Dou, Wayne Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen on IR and NLP.

I’m on the industry job market (2024)! Please feel free to reach out!

Google Scholar | Semantic Scholar | Github | CV

Research Interests


Publications (* indicates equal contribution)

  1. Instruction-tuned Language Models are Better Knowledge Learners [PDF][Code]
    Zhengbao Jiang, Zhiqing Sun, Weijia Shi, Pedro Rodriguez, Chunting Zhou, Graham Neubig, Xi Victoria Lin, Wen-tau Yih, Srinivasan Iyer
  2. Active Retrieval Augmented Generation [PDF][Code]
    Zhengbao Jiang*, Frank F. Xu*, Luyu Gao*, Zhiqing Sun*, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig
  3. From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning [PDF][Code]
    Qian Liu*, Fan Zhou*, Zhengbao Jiang, Longxu Dou, Min Lin
  4. GPTScore: Evaluate as You Desire [PDF][Code]
    Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, Pengfei Liu
  5. Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer [PDF][Code]
    Zhengbao Jiang*, Luyu Gao*, Jun Araki, Haibo Ding, Zhiruo Wang, Jamie Callan, Graham Neubig
    EMNLP’22 (oral)
  6. PEER: A Collaborative Language Model [PDF]
    Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, Sebastian Riedel
  7. EditEval: An Instruction-Based Benchmark for Text Improvements [PDF]
    Jane Dwivedi-Yu, Timo Schick, Zhengbao Jiang, Maria Lomeli, Patrick Lewis, Gautier Izacard, Edouard Grave, Sebastian Riedel, Fabio Petroni
  8. DocPrompting: Generating Code by Retrieving the Docs [PDF]
    Shuyan Zhou, Uri Alon, Frank F. Xu, Zhiruo Wang, Zhengbao Jiang, Graham Neubig
  9. OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering [PDF][Code]
    Zhengbao Jiang, Yi Mao, Pengcheng He, Graham Neubig, Weizhu Chen
    NAACL’22 (oral)
  10. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing [PDF][Website]
    Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, Graham Neubig
    ACM CSUR’22
  11. Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering [PDF][Code]
    Zhengbao Jiang, Jun Araki, Haibo Ding, Graham Neubig
    COLING’22 (oral)
  12. Table Retrieval May Not Necessitate Table-specific Model Design [PDF][Code]
    Zhiruo Wang, Zhengbao Jiang, Eric Nyberg, Graham Neubig
    NAACL’22 (SUKI workshop)
  13. How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering [PDF]
    Zhengbao Jiang, Jun Araki, Haibo Ding, Graham Neubig
  14. CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction [PDF]
    Zhengbao Jiang, Jialong Han, Bunyamin Sisman, Xin Luna Dong
  15. GSum: A General Framework for Guided Neural Abstractive Summarization [PDF]
    Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi, Zhengbao Jiang, Graham Neubig
  16. X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models [PDF][Code]
    Zhengbao Jiang*, Antonios Anastasopoulos*, Jun Araki, Haibo Ding, Graham Neubig
  17. How Can We Know What Language Models Know? [PDF][Code]
    Zhengbao Jiang*, Frank F. Xu*, Jun Araki, Graham Neubig
    TACL’20 (oral)
  18. Generalizing Natural Language Analysis through Span-relation Representations [PDF][Code]
    Zhengbao Jiang, Wei Xu, Jun Araki, Graham Neubig
  19. Incorporating External Knowledge through Pre-training for Natural Language to Code Generation [PDF][Code]
    Frank F. Xu*, Zhengbao Jiang*, Pengcheng Yin, Bogdan Vasilescu, Graham Neubig
    ACL’20 (short)
  20. Graph-Revised Convolutional Network [PDF][Code]
    Donghan Yu, Ruohong Zhang, Zhengbao Jiang, Yuexin Wu, Yiming Yang
  21. Learning Relation Entailment with Structured and Textual Information [PDF][Code]
    Zhengbao Jiang, Jun Araki, Donghan Yu, Ruohong Zhang, Wei Xu, Yiming Yang, Graham Neubig
  22. Improving Open Information Extraction via Iterative Rank-Aware Learning [PDF][Code]
    Zhengbao Jiang, Pengcheng Yin, Graham Neubig
    ACL’19 (short)
  23. Personalizing Search Results Using Hierarchical RNN with Query-aware Attention [PDF]
    Songwei Ge, Zhicheng Dou, Zhengbao Jiang, Jian-Yun Nie, Ji-Rong Wen
  24. Supervised Search Result Diversification via Subtopic Attention [PDF]
    Zhengbao Jiang, Zhicheng Dou, Wayne Xin Zhao, Jian-Yun Nie, Ming Yue, Ji-Rong Wen
  25. Learning to Diversify Search Results via Subtopic Attention [PDF][Slide][Code]
    Zhengbao Jiang, Ji-Rong Wen, Zhicheng Dou, Wayne Xin Zhao, Jian-Yun Nie, Ming Yue
  26. Generating Query Facets Using Knowledge Bases [PDF]
    Zhengbao Jiang, Zhicheng Dou, Ji-Rong Wen
  27. Automatically Mining Facets for Queries from Their Search Results [PDF]
    Zhicheng Dou, Zhengbao Jiang, Sha Hu, Ji-Rong Wen, Ruihua Song