Jeffrey Wu: h-index, Total Citations, and Citation Map
Jeffrey Wu's h-index is 20 (20 i10-index, 202,872+ total citations across 34+ publications) according to Google Scholar as of July 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 202,872 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
- 202,872
- Citing Countries
- 65
As of July 2026.
Jeffrey Wu has an h-index of 20 and 202,872 total citations across 34 publications, with research cited by institutions in 65 countries.
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Global Impact Map
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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.
28 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 tuning and weak-to-strong generalization, establishing foundational methods for aligning AI capabilities with human intent.
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.
The researcher established the foundational framework for language models as unsupervised multitask learners, a seminal contribution that has been widely adopted by the independent research community.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Jeffrey Wu's research
Jeffrey Wu is a researcher at Anthropic AI, OpenAI. Their work has been cited 202,872 times across 34 publications (h-index 20), according to Google Scholar.
Their most-cited work, “Language models are few-shot learners” (2020), has accumulated 77,508 citations. Other influential works include “Language models are few-shot learners” (2020) with 76,558 citations and “Language models are unsupervised multitask learners” (2019) with 39,151 citations.
Citations of Jeffrey Wu'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.
