Antoine Bosselut
Stanford University
93 Papers
276 Citations
Antoine Bosselut is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Commonsense knowledge. The author has an hindex of 22, co-authored 60 publications. Previous affiliations of Antoine Bosselut include École Polytechnique Fédérale de Lausanne & Allen Institute for Artificial Intelligence.
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Papers
•Posted Content
On the Opportunities and Risks of Foundation Models.
Rishi Bommasani,Drew A. Hudson,Ehsan Adeli,Russ B. Altman,Simran Arora,Sydney von Arx,Michael S. Bernstein,Jeannette Bohg,Antoine Bosselut,Emma Brunskill,Erik Brynjolfsson,Shyamal Buch,Dallas Card,Rodrigo Castellon,Niladri S. Chatterji,Annie Chen,Kathleen Creel,Jared Davis,Dora Demszky,Chris Donahue,Moussa Doumbouya,Esin Durmus,Stefano Ermon,John Etchemendy,Kawin Ethayarajh,Li Fei-Fei,Chelsea Finn,Trevor Gale,Lauren Gillespie,Karan Goel,Noah D. Goodman,Shelby Grossman,Neel Guha,Tatsunori Hashimoto,Peter Henderson,John Hewitt,Daniel E. Ho,Jenny Hong,Kyle Hsu,Jing Huang,Thomas Icard,Saahil Jain,Dan Jurafsky,Pratyusha Kalluri,Siddharth Karamcheti,Geoff Keeling,Fereshte Khani,Omar Khattab,Pang Wei Koh,Mark Krass,Ranjay Krishna,Rohith Kuditipudi,Ananya Kumar,Faisal Ladhak,Mina Lee,Tony Lee,Jure Leskovec,Isabelle Levent,Xiang Lisa Li,Xuechen Li,Tengyu Ma,Ali Ahmad Malik,Christopher D. Manning,Suvir Mirchandani,Eric Mitchell,Zanele Munyikwa,Suraj Nair,Avanika Narayan,Deepak Narayanan,Ben Newman,Allen Nie,Juan Carlos Niebles,Hamed Nilforoshan,Julian Nyarko,Giray Ogut,Laurel Orr,Isabel Papadimitriou,Joon Sung Park,Chris Piech,Eva Portelance,Christopher Potts,Aditi Raghunathan,Rob Reich,Hongyu Ren,Frieda Rong,Yusuf H. Roohani,Camilo Ruiz,Jack Ryan,Christopher Ré,Dorsa Sadigh,Shiori Sagawa,Keshav Santhanam,Andy Shih,Krishnan Srinivasan,Alex Tamkin,Rohan Taori,Armin W. Thomas,Florian Tramèr,Rose E. Wang,William Yang Wang,Bohan Wu,Jiajun Wu,Yuhuai Wu,Sang Michael Xie,Michihiro Yasunaga,Jiaxuan You,Matei Zaharia,Michael Zhang,Tianyi Zhang,Xikun Zhang,Yuhui Zhang,Lucia Zheng,Kaitlyn Zhou,Percy Liang +113 more
TL;DR: The authors provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e. g.g. model architectures, training procedures, data, systems, security, evaluation, theory) to their applications.
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COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
Antoine Bosselut,Hannah Rashkin,Maarten Sap,Chaitanya Malaviya,Asli Celikyilmaz,Yejin Choi +5 more
- 12 Jun 2019
TL;DR: This investigation reveals promising results when implicit knowledge from deep pre-trained language models is transferred to generate explicit knowledge in commonsense knowledge graphs, and suggests that using generative commonsense models for automatic commonsense KB completion could soon be a plausible alternative to extractive methods.
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering.
Michihiro Yasunaga,Hongyu Ren,Antoine Bosselut,Percy Liang,Jure Leskovec +4 more
- 01 Jun 2021
TL;DR: This work proposes a new model, QA-GNN, which addresses the problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) through two key innovations: relevance scoring and joint reasoning.
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•Posted Content
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
TL;DR: The authors proposed COMmonsEnse Transformers (COMET) that learn to generate rich and diverse commonsense descriptions in natural language, and showed promising results when implicit knowledge from deep pre-trained language models is transferred to generate explicit knowledge in commonsense knowledge graphs.
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Deep Communicating Agents for Abstractive Summarization
Asli Celikyilmaz,Antoine Bosselut,Xiaodong He,Yejin Choi +3 more
- 14 Jul 2018
TL;DR: In this article, the task of encoding a long text is divided across multiple collaborating agents, each in charge of a subsection of the input text, connected to a single decoder, trained end-to-end using reinforcement learning.