Noah A. Smith: h-index, Total Citations, and Citation Map
Noah A. Smith's h-index is 137 (400 i10-index, 88,420+ total citations across 524+ publications) according to Google Scholar as of September 2026. Noah A. Smith is affiliated with University of Washington; Allen Institute for Artificial Intelligence.
Noah A. Smith is a researcher affiliated with University of Washington; Allen Institute for Artificial Intelligence, specializing in natural language processing, machine learning, computational social science. Their work has been cited 88,420 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Noah A. Smith's Citation Metrics
Bibliometric impact based on 524 indexed publications.
- H-Index
- 137
- i10-Index
- 400
- Total Citations
- 88,420
- Citing Countries
- 81
As of September 2026.
Noah A. Smith has an h-index of 137 and 88,420 total citations across 524 publications, with research cited by institutions in 81 countries.
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Global Impact Map
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Don't stop pretraining: adapt language models to domains and tasks
20203,896
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.
41 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered critical evaluations of racial bias and toxic degeneration in language models, establishing a foundational framework for assessing ethical risks in NLP systems.
The researcher pioneered the methodology of continuous pretraining to adapt language models to specific domains and tasks, establishing a foundational approach for model specialization.
The researcher pioneered the methodological linkage of social media text sentiment to public opinion time series, establishing a foundational framework for computational social science.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Noah A. Smith's research
Noah A. Smith is a researcher in natural language processing, machine learning and computational social science at University of Washington; Allen Institute for Artificial Intelligence. Their work has been cited 88,420 times across 524 publications (h-index 137), according to Google Scholar.
Their most-cited work, “Don't stop pretraining: adapt language models to domains and tasks” (2020), has accumulated 3,896 citations. Other influential works include “Self-instruct: Aligning language models with self-generated instructions” (2023) with 3,867 citations and “Self-instruct: Aligning language models with self-generated instructions” (2023) with 3,669 citations.
Citations of Noah A. Smith's research come primarily from United States, China 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.
