Shi Feng

I am a postdoc in Computer Science at University Chicago working with Chenhao Tan. I got my PhD from University of Maryland advised by Jordan Boyd-Graber.

I work on AI interpretability and safety, with a focus on NLP models. Our recent tutorial at NAACL gives a good overview for problems that I care about.

I'm on the job market this year.

Papers

  • Learning Human-Compatible Representations for Case-Based Decision Support
    Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, Chenhao Tan
    ICLR 2023 [openreview]

  • Pragmatic Machine Explanations
    Shi Feng, Chenhao Tan
    To appear at HCAI@NeurIPS 2022 [pdf]

  • Learning to Explain Selectively
    Shi Feng, Jordan Boyd-Graber
    EMNLP 2022 [pdf]

  • Active Example Selection for In-Context Learning
    Yiming Zhang, Shi Feng, Chenhao Tan
    EMNLP 2022 [arxiv]

  • Human Learning Meets Representation Learning
    Matthew Shu*, Shi Feng*, Jordan Boyd-Graber
    preprint

  • Machine Explanations and Human Understanding
    Chacha Chen*, Shi Feng*, Amit Sharma, Chenhao Tan
    HMCaT @ ICML 2022, best paper [arxiv]

  • Calibrate Before Use: Improving Few-shot Performance of Language Models
    Tony Z. Zhao*, Eric Wallace*, Shi Feng, Dan Klein, Sameer Singh
    ICML 2021 [pmlr] [pdf]

  • Concealed Data Poisoning Attacks on NLP Models
    Eric Wallace*, Tony Z. Zhao*, Shi Feng, Sameer Singh
    NAACL 2021 [acl] [pdf] [blog]

  • Universal Adversarial Triggers for Attacking and Analyzing NLP
    Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh
    EMNLP 2019 [acl] [pdf] [blog]

  • Misleading Failures of Partial-input Baselines
    Shi Feng, Eric Wallace, Jordan Boyd-Graber
    ACL 2019, short paper [acl] [pdf]

  • Quizbowl: The Case for Incremental Question Answering
    Pedro Rodriguez, Shi Feng, Mohit Iyyer, He He, Jordan Boyd-Graber
    In submission [arxiv]

  • Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation
    Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi
    ICML 2019 [pmlr] [pdf]

  • Trick Me If You Can: Human-in-the-loop Generation of Adversarial Examples for
    Question Answering

    Eric Wallace, Pedro Rodriguez, Shi Feng, Jordan Boyd-Graber
    TACL 2019 [acl] [pdf]

  • What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play
    Shi Feng, Jordan Boyd-Graber
    IUI 2019 [arxiv]

  • Pathologies of Neural Models Make Interpretation Difficult
    Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, Jordan Boyd-Graber
    EMNLP 2018, oral [acl] [pdf] [talk]

  • Interpreting Neural Networks with Nearest Neighbors
    Eric Wallace*, Shi Feng*, Jordan Boyd-Graber
    BlackboxNLP @ EMNLP 2018 [acl] [pdf]

  • The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
    Amr Sharaf, Shi Feng, Khanh Nguyen, Kianté Brantley, Hal Daumé III
    WMT @ EMNLP 2017 [acl] [pdf]

  • Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation
    Shi Feng, Shujie Liu, Nan Yang, Mu Li, Ming Zhou, Kenny Q. Zhu
    COLING 2016 [acl] [pdf]

Experience

  • June 1 2020 Intern @ Salesforce Research
  • Apr 25 2019 NLP Highlights Podcast on interpretation of NLP models
  • Mar 2019 Invited talks at UPenn, UCSD, UCI
  • Best reviewer award @ EMNLP 2018, 2020
  • Summer 2018 Research Intern @ Microsoft Research