Jeffrey Wu: h-index, Total Citations, and Citation Map
Jeffrey Wu's h-index is 20 (20 i10-index, 197,690+ total citations across 34+ publications) according to Google Scholar as of June 2026. Jeffrey Wu is affiliated with Anthropic AI, OpenAI.
Jeffrey Wu is a researcher affiliated with Anthropic AI, OpenAI, specializing in various fields. Their work has been cited 197,690 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Jeffrey Wu's Citation Metrics
Bibliometric impact based on 34 indexed publications.
- H-Index
- 20
- i10-Index
- 20
- Total Citations
- 197,690
- Citing Countries
- 41
As of June 2026.
Jeffrey Wu has an h-index of 20 and 197,690 total citations across 34 publications, with research cited by institutions in 41 countries.
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We've mapped 5,000 of 197,690 citations for Jeffrey Wu
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Language models are few-shot learners
202077,508
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
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.
13 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered scalable few-shot learning in language models and advanced instruction-following capabilities through human feedback, establishing foundational methods for generalizing model behavior.
The researcher advanced the field by demonstrating that large-scale language models can perform complex tasks with minimal examples, establishing few-shot learning as a viable paradigm for general-purpose AI.
The researcher established that language models function as unsupervised multitask learners, a foundational framework that has profoundly influenced the development of modern natural language processing systems.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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