Luke Zettlemoyer: h-index, Total Citations, and Citation Map
Luke Zettlemoyer's h-index is 143 (318 i10-index, 195,752+ total citations across 516+ publications) according to Google Scholar as of September 2026. Luke Zettlemoyer is affiliated with University of Washington; Meta.
Luke Zettlemoyer is a researcher affiliated with University of Washington; Meta, specializing in various fields. Their work has been cited 195,752 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Luke Zettlemoyer's Citation Metrics
Bibliometric impact based on 516 indexed publications.
- H-Index
- 143
- i10-Index
- 318
- Total Citations
- 195,752
- Citing Countries
- 73
As of September 2026.
Luke Zettlemoyer has an h-index of 143 and 195,752 total citations across 516 publications, with research cited by institutions in 73 countries.
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Global Impact Map
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Roberta: A robustly optimized bert pretraining approach
201944,399
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
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Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
15 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher advanced sequence-to-sequence pre-training via BART and subsequently investigated the mechanisms underlying in-context learning, establishing a foundational line of inquiry in natural language generation.
The researcher advanced neural coreference resolution and contextual word representations, establishing foundational methods widely adopted by independent researchers across the natural language processing community.
The researcher developed RoBERTa, a robustly optimized BERT pretraining approach that significantly advanced natural language processing benchmarks and established new standards for transformer-based language model training.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Luke Zettlemoyer's research
Luke Zettlemoyer is a researcher at University of Washington; Meta. Their work has been cited 195,752 times across 516 publications (h-index 143), according to Google Scholar.
Their most-cited work, “Roberta: A robustly optimized bert pretraining approach” (2019), has accumulated 44,399 citations. Other influential works include “Deep contextualized word representations” (2018) with 18,705 citations and “BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension” (2020) with 16,636 citations.
Citations of Luke Zettlemoyer's research come primarily from China, United States and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
