Congestion control in BBN packet-switched networks. - J. Robinson; Dan Friedman; Martha Steenstrup (Comput. Commun. Rev., 1990)

Linguistically Rich Vector Representations of Supertags for TAG Parsing. - Dan Friedman; Jungo Kasai; R. Thomas McCoy; Robert Frank 001; Forrest Davis; Owen Rambow (TAG, 2017)

Deep density networks and uncertainty in recommender systems. - Yoel Zeldes; Stavros Theodorakis; Efrat Solodnik; Aviv Rotman; Gil Chamiel; Dan Friedman (CoRR, 2017)

ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks. - Michihiro Yasunaga; Jungo Kasai; Rui Zhang 037; Alexander R. Fabbri; Irene Li; Dan Friedman; Dragomir R. Radev (AAAI, 2019)

Syntax-aware Neural Semantic Role Labeling with Supertags. - Jungo Kasai; Dan Friedman; Robert Frank 001; Dragomir R. Radev; Owen Rambow (NAACL-HLT (1), 2019)

Single-dataset Experts for Multi-dataset Question Answering. - Dan Friedman; Ben Dodge; Danqi Chen 001 (EMNLP (1), 2021)

Factual Probing Is [MASK]: Learning vs. Learning to Recall. - Zexuan Zhong; Dan Friedman; Danqi Chen 001 (NAACL-HLT, 2021)

Finding Dataset Shortcuts with Grammar Induction. - Dan Friedman; Alexander Wettig; Danqi Chen 001 (EMNLP, 2022)

Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. - Chenglei Si; Dan Friedman; Nitish Joshi; Shi Feng 005; Danqi Chen 001; He He 001 (ACL (1), 2023)

Learning Transformer Programs. - Dan Friedman; Alexander Wettig; Danqi Chen 001 (NeurIPS, 2023)

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 001 (CoRR, 2023)

The Vendi Score: A Diversity Evaluation Metric for Machine Learning. - Dan Friedman; Adji Bousso Dieng (Trans. Mach. Learn. Res., 2023)

The Heuristic Core: Understanding Subnetwork Generalization in Pretrained Language Models. - Adithya Bhaskar; Dan Friedman; Danqi Chen 001 (ACL (1), 2024)

Interpretability Illusions in the Generalization of Simplified Models. - Dan Friedman; Andrew Kyle Lampinen; Lucas Dixon; Danqi Chen 001; Asma Ghandeharioun (ICML, 2024)

Finding Transformer Circuits With Edge Pruning. - Adithya Bhaskar; Alexander Wettig; Dan Friedman; Danqi Chen 001 (NeurIPS, 2024)

When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1. - R. Thomas McCoy; Shunyu Yao; Dan Friedman; Mathew D. Hardy; Thomas L. Griffiths 001 (CoRR, 2024)

Continual Memorization of Factoids in Large Language Models. - Howard Chen 003; Jiayi Geng; Adithya Bhaskar; Dan Friedman; Danqi Chen 001 (CoRR, 2024)

Representing Rule-based Chatbots with Transformers. - Dan Friedman; Abhishek Panigrahi; Danqi Chen 001 (NAACL (Long Papers), 2025)