Bio

Originally from Beijing, China, I am currently a Ph.D. student in the NLP group at MIT CSAIL. I received my M.S. in computer science, B.S. in computer science, and B.A. in linguistics from the University of Washington.

I love computers, languages, and teaching computers to understand languages. On this path, I have done research at the Allen Institute for Artificial Intelligence (AI2) (as a PYI), and at the University of Washington with Prof. Noah Smith and Prof. Fei Xia. I have also worked on many interesting NLP and machine learning problems in the industry when I interned at Google, Facebook Meta, and other places.

I co-organize NLP with Friends, a seminar series where we chat about NLP research. Come join us!

Two friends and I have also built a platform, cs-sop.org, where past applicants to CS PhD programs have generously shared their statements of purpose and other advice. We hope this could be a useful resource for future applicants.

Publications

• Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks
  Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, and Yoon Kim
  Unpublished manuscript, 2023.
• Transparency Helps Reveal When Language Models Learn Meaning
  Zhaofeng Wu, William Merrill, Hao Peng, Iz Beltagy, and Noah A. Smith
  In Transactions of the Association for Computational Linguistics (TACL), 2023.
  [slides] [poster]
• We're Afraid Language Models Aren't Modeling Ambiguity
  Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah A. Smith, and Yejin Choi
  To appear in Empirical Methods in Natural Language Processing (EMNLP), 2023.
• Continued Pretraining for Better Zero- and Few-Shot Promptability
  Zhaofeng Wu, Robert L. Logan IV, Pete Walsh, Akshita Bhagia, Dirk Groeneveld, Sameer Singh, and Iz Beltagy
  In Empirical Methods in Natural Language Processing (EMNLP), 2022.
  [poster]
• Modeling Context With Linear Attention for Scalable Document-Level Translation
  Zhaofeng Wu, Hao Peng, Nikolaos Pappas, and Noah A. Smith
  In Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP Findings), 2022.
  [poster]
• ABC: Attention with Bounded-memory Control
  Hao Peng, Jungo Kasai, Nikolaos Pappas, Dani Yogatama, Zhaofeng Wu, Lingpeng Kong, Roy Schwartz, and Noah A. Smith
  In Annual Meeting of the Association for Computational Linguistics (ACL), 2022.
• Learning with Latent Structures in Natural Language Processing: A Survey
  Zhaofeng Wu
  Unpublished manuscript, 2022.
• Infusing Finetuning with Semantic Dependencies
  Zhaofeng Wu, Hao Peng, and Noah A. Smith
  In Transactions of the Association for Computational Linguistics (TACL), 2021.
  [slides]
• Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution
  Zhaofeng Wu and Matt Gardner
  In Workshop on Computational Models of Reference, Anaphora and Coreference @ EMNLP, 2021.
  [slides]
• Dynamic Sparsity Neural Networks for Automatic Speech Recognition
  Zhaofeng Wu, Ding Zhao, Qiao Liang, Jiahui Yu, Anmol Gulati, and Ruoming Pang
  In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.
  [slides]
• WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language Inference
  Zhaofeng Wu, Yan Song, Sicong Huang, Yuanhe Tian, and Fei Xia
  In BioNLP Workshop and Shared Task @ ACL, 2019.

Talks

Language Models: A Reality Check
UIUC, NYU, Cornell, Princeton
September 2023

Graph Neural Networks for NLP
UW CSE 481N: NLP Capstone
May 2021