I'm currently interested in making large, neural language models easier to understand.
One direction is to design models that are
so that we can automatically convert models into formats that are easier to inspect and understand, such as discrete computer programs.
I'm also interested in approaches that take a more behavioral view, to better characterize the strengths and limitations of large language models
Some of my more general interests include unsupervised structure learning, formal languages,
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
I'm on GitHub,
Learning Transformer Programs.
Dan Friedman, Alexander Wettig, Danqi Chen
NeurIPS 2023 Oral (to appear)
Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve.
R. Thomas McCoy, Shunyu Yao, Dan Friedman, Matthew Hardy, Thomas L. Griffiths
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations.
Chenglei Si*, Dan Friedman*, Nitish Joshi, Shi Feng, Danqi Chen, He He
The Vendi Score: A Diversity Evaluation Metric for Machine Learning.
Dan Friedman, Adji Bousso Dieng
Transactions of Machine Learning Research (TMLR) 2023
Finding Dataset Shortcuts with Grammar Induction.
Dan Friedman, Alexander Wetting, Danqi Chen
Single-dataset Experts for Multi-dataset Question Answering.
Dan Friedman, Ben Dodge, Danqi Chen
Factual Probing is [MASK]: Learning vs. Learning
Zexuan Zhong*, Dan Friedman*, Danqi Chen
Syntax-aware Neural Semantic Role Labeling with Supertags
Jungo Kasai, Dan Friedman, Robert Frank,
Dragomir Radev, Owen Rambow
ScisummNet: A Large Annotated Corpus and Content-Impact
Models for Scientific Paper Summarization with Citation
Michihiro Yasunaga, Jungo Kasai, Rui Zhang, Alexander R
Fabbri, Irene Li, Dan Friedman, Dragomir R
Linguistically Rich Vector Representations of Supertags for
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