Displaying Asynchronous Reactions to a Document: Two Goals and a Design - Todd Davies; Benjamin Newman; Brendan O'Connor 001; Aaron Tam; Leo Perry (CoRR, 2013)

An Online Environment for Democratic Deliberation: Motivations, Principles, and Design - Todd Davies; Brendan O'Connor 001; Alex Cochran; Jonathan J. Effrat; Andrew Parker; Benjamin Newman; Aaron Tam (CoRR, 2013)

Global and local statistical regularities control visual attention to object sequences. - Alexa R. Romberg; Yayun Zhang; Benjamin Newman; Jochen Triesch; Chen Yu 001 (ICDL-EPIROB, 2016)

Communication-based Evaluation for Natural Language Generation. - Benjamin Newman; Reuben Cohn-Gordon; Christopher Potts (CoRR, 2019)

The EOS Decision and Length Extrapolation. - Benjamin Newman; John Hewitt; Percy Liang; Christopher D. Manning (BlackboxNLP@EMNLP, 2020)

Optimal Assistance for Object-Rearrangement Tasks in Augmented Reality. - Benjamin Newman; Kevin Carlberg; Ruta Desai (CoRR, 2020)

Refining Targeted Syntactic Evaluation of Language Models. - Benjamin Newman; Kai-Siang Ang; Julia Gong; John Hewitt (NAACL-HLT, 2021)

P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts. - Benjamin Newman; Prafulla Kumar Choubey; Nazneen Rajani (ICLR, 2022)

Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. - Yuji Zhao; Max A. Laansma; Eva M. van Heese; Conor Owens-Walton; Laura M. Parkes; Ines Debove; Christian Rummel; Roland Wiest; Fernando Cendes; Rachel Guimaraes; Clarissa Lin Yasuda; Jiun-Jie Wang; et al. (MLCN@MICCAI, 2022)

Comparing Sentence-Level Suggestions to Message-Level Suggestions in AI-Mediated Communication. - Liye Fu; Benjamin Newman; Maurice Jakesch; Sarah Kreps (CHI, 2023)

PaperMage: A Unified Toolkit for Processing, Representing, and Manipulating Visually-Rich Scientific Documents. - Kyle Lo; Zejiang Shen 001; Benjamin Newman; Joseph Chee Chang; Russell Authur; Erin Bransom; Stefan Candra; Yoganand Chandrasekhar; Regan Huff; Bailey Kuehl; Amanpreet Singh; Chris Wilhelm; et al. (EMNLP (Demos), 2023)

A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents. - Benjamin Newman; Luca Soldaini; Raymond Fok; Arman Cohan; Kyle Lo (EMNLP, 2023)

A Controllable QA-based Framework for Decontextualization. - Benjamin Newman; Luca Soldaini; Raymond Fok; Arman Cohan; Kyle Lo (CoRR, 2023)

Holistic Evaluation of Language Models. - Percy Liang; Rishi Bommasani; Tony Lee; Dimitris Tsipras; Dilara Soylu; Michihiro Yasunaga; Yian Zhang; Deepak Narayanan; Yuhuai Wu; Ananya Kumar; Benjamin Newman; Binhang Yuan; et al. (Trans. Mach. Learn. Res., 2023)

Editorial: Is Now the Time for Foundational Theory of Brain Connectivity? - John Darrell Van Horn; Zachary J. Jacokes; Benjamin Newman; Teague Henry (Neuroinformatics, 2023)

ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models. - Benjamin Newman; Yoonjoo Lee; Aakanksha Naik; Pao Siangliulue; Raymond Fok; Juho Kim 001; Daniel S. Weld; Joseph Chee Chang; Kyle Lo (EMNLP, 2024)

The Generative AI Paradox: "What It Can Create, It May Not Understand". - Peter West; Ximing Lu; Nouha Dziri; Faeze Brahman; Linjie Li; Jena D. Hwang; Liwei Jiang; Jillian Fisher; Abhilasha Ravichander; Khyathi Raghavi Chandu; Benjamin Newman; Pang Wei Koh; et al. (ICLR, 2024)

WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries. - Wenting Zhao; Tanya Goyal; Yu Ying Chiu; Liwei Jiang; Benjamin Newman; Abhilasha Ravichander; Khyathi Raghavi Chandu; Ronan Le Bras 001; Claire Cardie; Yuntian Deng; Yejin Choi 001 (CoRR, 2024)

Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics? - Rachel M. Edelstein; Sterling Gutterman; Benjamin Newman; John Darrell Van Horn (Neuroinformatics, 2024)

Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers. - Wooseok Seo; Seungju Han 002; Jaehun Jung; Benjamin Newman; Seungwon Lim; Seungbeen Lee; Ximing Lu; Ye Jin Choi 001; Youngjae Yu (CoRR, 2025)

The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality. - Benjamin Newman; Abhilasha Ravichander; Jaehun Jung; Rui Xin; Hamish Ivison; Yegor Kuznetsov; Pang Wei Koh; Yejin Choi 001 (CoRR, 2025)