| 2025 | NAACL | Representing Rule-based Chatbots with Transformers. | Dan Friedman, Abhishek Panigrahi, Danqi Chen |
| 2024 | ACL | The Heuristic Core: Understanding Subnetwork Generalization in Pretrained Language Models. | Adithya Bhaskar, Dan Friedman, Danqi Chen |
| 2024 | ICML | Interpretability Illusions in the Generalization of Simplified Models. | Dan Friedman, Andrew Kyle Lampinen, Lucas Dixon, Danqi Chen, Asma Ghandeharioun |
| 2023 | ACL | Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. | Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He |
| 2022 | EMNLP | Finding Dataset Shortcuts with Grammar Induction. | Dan Friedman, Alexander Wettig, Danqi Chen |
| 2021 | EMNLP | Single-dataset Experts for Multi-dataset Question Answering. | Dan Friedman, Ben Dodge, Danqi Chen |
| 2021 | NAACL | Factual Probing Is [MASK]: Learning vs. Learning to Recall. | Zexuan Zhong, Dan Friedman, Danqi Chen |
| 2019 | AAAI | 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 |
| 2019 | NAACL | Syntax-aware Neural Semantic Role Labeling with Supertags. | Jungo Kasai, Dan Friedman, Robert Frank, Dragomir R. Radev, Owen Rambow |