Xilun Chen
RESEARCH SCIENTIST | SEATTLE, UNITED STATES
Xilun Chen is a Research Scientist at Meta AI. His research interests are in Natural Language Processing and Machine Learning. His work focuses on knowledge-intensive NLP tasks such as Information Retrieval and Question Answering, and more recently retrieval-augmented language models. He received his Ph.D. from Cornell University in 2019.
Research Areas
Xilun's Publications
June 14, 2024
NLP
How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval
Sheng-Chieh Lin, Akari Asai, Minghan Li, Barlas Oguz, Jimmy Lin, Scott Yih, Xilun Chen
June 14, 2024
June 05, 2024
CORE MACHINE LEARNING
An Introduction to Vision-Language Modeling
Florian Bordes, Richard Pang, Anurag Ajay, Alexander C. Li, Adrien Bardes, Suzanne Petryk, Oscar Mañas, Zhiqiu Lin, Anas Mahmoud, Bargav Jayaraman, Mark Ibrahim, Melissa Hall, Yunyang Xiong, Jonathan Lebensold, Candace Ross, Srihari Jayakumar, Chuan Guo, Diane Bouchacourt, Haider Al-Tahan, Karthik Padthe, Vasu Sharma, Hu Xu, Ellen Tan, Megan Richards, Samuel Lavoie, Pietro Astolfi, Reyhane Askari, Jun Chen, Kushal Tirumala, Rim Assouel, Mazda Moayeri, Arjang Talattof, Kamalika Chaudhuri, Zechun Liu, Xilun Chen, Quentin Garrido, Karen Ullrich, Aishwarya Agrawal, Kate Saenko, Asli Celikyilmaz, Vikas Chandra
June 05, 2024
June 20, 2023
NLP
CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval
Minghan Li, Jack Lin, Barlas Oguz, Asish Ghoshal, Jimmy Lin, Yashar Mehdad, Scott Yih, Xilun Chen
June 20, 2023
July 07, 2022
NLP
CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training
Xilun Chen, Armen Aghajanyan, Barlas Oguz, Scott Yih, Sonal Gupta, Patrick Huber
July 07, 2022
May 06, 2022
Domain-matched Pre-training Tasks for Dense Retrieval
Barlas Oguz, Aleksandra Piktus, Anchit Gupta, Kushal Lakhotia, Patrick Lewis, Scott Yih, Sebastian Riedel, Sonal Gupta, Vladimir Karpukhin, Xilun Chen, Yashar Mehdad
May 06, 2022
Product experiences
Foundational models
Product experiences
Latest news
Foundational models
Meta © 2024