Dan Friedman

I'm a third-year PhD student in the Princeton NLP group, working with Danqi Chen. Some of my current interests include unsupervised structure learning, probabilistic models, and inductive bias. I'm also interested in applications of NLP to humanities research and am involved with the Princeton Center for Digital Humanities . Before this I was a software engineer at Google and IBM, and before that I was an undergraduate at Yale where I worked with Bob Frank and Dragomir Radev, and received a BA in English.

I am on GitHub, Twitter, and email.

Publications

• Learning Transformer Programs.
  Dan Friedman, Alexander Wettig, Danqi Chen
  arXiv 2023
• Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations.
  Chenglei Si*, Dan Friedman*, Nitish Joshi, Shi Feng, Danqi Chen, He He
  ACL 2023
• Finding Dataset Shortcuts with Grammar Induction.
  Dan Friedman, Alexander Wetting, Danqi Chen
  EMNLP 2022
• The Vendi Score: A Diversity Evaluation Metric for Machine Learning.
  Dan Friedman, Adji Bousso Dieng
  arXiv 2022
• Single-dataset Experts for Multi-dataset Question Answering.
  Dan Friedman, Ben Dodge, Danqi Chen
  EMNLP 2021
• Factual Probing is [MASK]: Learning vs. Learning to Recall.
  Zexuan Zhong*, Dan Friedman*, Danqi Chen
  NAACL 2021
• Syntax-aware Neural Semantic Role Labeling with Supertags
  Jungo Kasai, Dan Friedman, Robert Frank, Dragomir Radev, Owen Rambow
  NAACL 2019
• ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks
  Michihiro Yasunaga, Jungo Kasai, Rui Zhang, Alexander R Fabbri, Irene Li, Dan Friedman, Dragomir R Radev
  AAAI 2019
• Linguistically Rich Vector Representations of Supertags for TAG Parsing
  Dan Friedman^*, Jungo Kasai^*, R Thomas McCoy^*, Robert Frank, Forrest Davis, Owen Rambow
  Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms (2017)