Scott Wen-tau Yih
RESEARCH SCIENTIST | SEATTLE, UNITED STATES
Scott is a Research Scientist at Facebook AI Research (FAIR) his general research interests include natural language processing, machine learning and information retrieval. He has worked on a variety of problems over the years, including information extraction, semantic role labeling, email spam filtering, keyword extraction and search & ad relevance. His recent work focuses on continuous representations and neural network models, with applications in knowledge base embedding, semantic parsing and question answering.
Research Areas
Scott's Work
Scott's Publications
December 17, 2024
NLP
FLAME : Factuality-Aware Alignment for Large Language Models
Jack Lin, Luyu Gao, Barlas Oguz, Wenhan Xiong, Jimmy Lin, Scott Yih, Xilun Chen
December 17, 2024
December 11, 2024
NLP
COMPUTER VISION
Meta CLIP 1.2
Hu Xu, Bernie Huang, Ellen Tan, Ching-Feng Yeh, Jacob Kahn, Christine Jou, Gargi Ghosh, Omer Levy, Luke Zettlemoyer, Scott Yih, Philippe Brunet, Kim Hazelwood, Ramya Raghavendra, Daniel Li (FAIR), Saining Xie, Christoph Feichtenhofer
December 11, 2024
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
November 17, 2023
NLP
FactScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Scott Yih, Luke Zettlemoyer, Mike Lewis, Hannaneh Hajishirzi, Kalpesh Krishna, Mohit Iyyer, Pang Wei Koh, Sewon Min, Xinxi Lyu
November 17, 2023
September 03, 2023
NLP
COMPUTER VISION
Retrieval-Augmented Multimodal Language Modeling
Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Rich James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, Scott Yih
September 03, 2023
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
June 06, 2023
NLP
Task-aware Retrieval with Instructions
Akari Asai, Timo Schick, Patrick Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, Scott Yih
June 06, 2023
June 05, 2023
NLP
Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering
Yung-Sung Chuang, Wei Fang, Daniel Li (FAIR), Scott Yih, James Glass
June 05, 2023
October 31, 2022
RESEARCH
NLP
Autoregressive Search Engines: Generating Substrings as Document Identifiers
Fabio Petroni, Giuseppe Ottaviano, Michele Bevilacqua, Patrick Lewis, Scott Yih, Sebastian Riedel
October 31, 2022
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 22, 2022
UNIPELT: A Unified Framework for Parameter-Efficient Language Model Tuning
Madian Khabsa, Amjad Almahairi, Hao Ma, Lambert Mathias, Rui Hou, Scott Yih, Yuning Mao, Jiawei Han
May 22, 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
October 15, 2020
CONVERSATIONAL AI
NLP
An Imitation Game for Learning Semantic Parsers from User Interaction
Scott Yih, Huan Sun, Yiqi Tang, Yu Su, Ziyu Yao
October 15, 2020
June 25, 2020
NLP
Language Models as Fact Checkers?
Madian Khabsa, Belinda Li, Hao Ma, Scott Yih, Sinong Wang, Nayeon Lee
June 25, 2020
May 06, 2020
RESEARCH
NLP
TaBert: Pretraining for Joint Understanding of Textual and Tabular Data
Scott Yih, Sebastian Riedel, Graham Neubig, Pengcheng Yin
May 06, 2020
Foundational models
Latest news
Foundational models
Meta © 2024