2026 Luke Zettlemoyer: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	113	196	191	113	110	286	87975

Overview

Luke Zettlemoyer is affiliated with the University of Washington in the United States. The primary field of their research is Computer Science, with a strong focus on subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research, and Electrical and Electronic Engineering.

The main topics covered in their work include:

Topic Modeling
Natural Language Processing Techniques
Multimodal Machine Learning Applications
Text Readability and Simplification
Speech Recognition and Synthesis
Domain Adaptation and Few-Shot Learning
Software Engineering Research

Significant recent papers authored or coauthored by Luke Zettlemoyer include:

Towards Learning Terminological Concept Systems from Multilingual Natural Language Text, 2021, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
Multilingual Denoising Pre-training for Neural Machine Translation, 2020, Transactions of the Association for Computational Linguistics
Analysing Off-The-Shelf Options for Question Answering with Portuguese FAQs, 2022, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
Multilingual Denoising Pre-training for Neural Machine Translation, 2020, arXiv (Cornell University)
QLoRA: Efficient Finetuning of Quantized LLMs, 2023, arXiv (Cornell University)

Frequent coauthors who have collaborated with Luke Zettlemoyer include:

Hannaneh Hajishirzi
Michael Lewis
Sewon Min
Noah A. Smith
Marjan Ghazvininejad

Luke Zettlemoyer has published extensively in several venues, with major contributions found in:

arXiv (Cornell University)
Transactions of the Association for Computational Linguistics
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Best Publications

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Yinhan Liu;Myle Ott;Naman Goyal;Jingfei Du

21673
Citations
Deep contextualized word representations

Matthew E. Peters;Mark Neumann;Mohit Iyyer;Matt Gardner

11936
Citations
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Mike Lewis;Yinhan Liu;Naman Goyal;Marjan Ghazvininejad

7256
Citations
Unsupervised Cross-lingual Representation Learning at Scale

Alexis Conneau;Kartikay Khandelwal;Naman Goyal;Vishrav Chaudhary

4500
Citations
SpanBERT: Improving Pre-training by Representing and Predicting Spans

Mandar Joshi;Danqi Chen;Yinhan Liu;Daniel S. Weld

1796
Citations
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Mandar Joshi;Eunsol Choi;Daniel S. Weld;Luke Zettlemoyer

1514
Citations
Multilingual Denoising Pre-training for Neural Machine Translation

Yinhan Liu;Jiatao Gu;Naman Goyal;Xian Li

1500
Citations
AllenNLP: A Deep Semantic Natural Language Processing Platform

Matt Gardner;Joel Grus;Mark Neumann;Oyvind Tafjord

1389
Citations
Learning to map sentences to logical form: structured classification with probabilistic categorial grammars

Luke S. Zettlemoyer;Michael Collins

1290
Citations
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Unknown

1138
Citations
Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations

Raphael Hoffmann;Congle Zhang;Xiao Ling;Luke Zettlemoyer

1062
Citations
End-to-end Neural Coreference Resolution

Kenton Lee;Luheng He;Mike Lewis;Luke Zettlemoyer

885
Citations
Toolformer: Language Models Can Teach Themselves to Use Tools

Unknown

874
Citations
QLoRA: Efficient Finetuning of Quantized LLMs

Unknown

810
Citations
QuAC: Question Answering in Context

Eunsol Choi;He He;Mohit Iyyer;Mohit Iyyer;Mark Yatskar

751
Citations
Summarizing Source Code using a Neural Attention Model

Srinivasan Iyer;Ioannis Konstas;Alvin Cheung;Luke Zettlemoyer

723
Citations
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks

Mohit Iyyer;John Wieting;Kevin Gimpel;Luke Zettlemoyer

669
Citations
Dissecting Contextual Word Embeddings: Architecture and Representation

Matthew E. Peters;Mark Neumann;Luke Zettlemoyer;Wen-tau Yih

553
Citations
Higher-Order Coreference Resolution with Coarse-to-Fine Inference

Kenton Lee;Luheng He;Luke Zettlemoyer

521
Citations
Mask-Predict: Parallel Decoding of Conditional Masked Language Models.

Marjan Ghazvininejad;Omer Levy;Yinhan Liu;Luke Zettlemoyer

513
Citations
Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions

Yoav Artzi;Luke Zettlemoyer

488
Citations
Open question answering over curated and extracted knowledge bases

Anthony Fader;Luke Zettlemoyer;Oren Etzioni

482
Citations
Zero-Shot Relation Extraction via Reading Comprehension

Omer Levy;Minjoon Seo;Eunsol Choi;Luke Zettlemoyer

475
Citations

Frequent Co-Authors

Omer Levy Deep Mind

Kenton Lee Google (United States)

Hannaneh Hajishirzi University of Washington

Yejin Choi Stanford University

Daniel S. Weld University of Washington

Leslie Pack Kaelbling MIT

Yoav Artzi Cornell University

Matt Gardner Allen Institute for Artificial Intelligence

Dieter Fox University of Washington

Ido Dagan Bar-Ilan University

External Links

Personal website of Luke Zettlemoyer Google Scholar page

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