Yejin Choi: h-index, Total Citations, and Citation Map
Yejin Choi's h-index is 139 (396 i10-index, 94,776+ total citations across 628+ publications) according to Google Scholar as of June 2026. Yejin Choi is affiliated with Stanford University / NVIDIA.
Yejin Choi is a researcher affiliated with Stanford University / NVIDIA, specializing in Natural Language Processing, Deep Learning, Artificial Intelligence. Their work has been cited 94,776 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Yejin Choi's Citation Metrics
Bibliometric impact based on 628 indexed publications.
- H-Index
- 139
- i10-Index
- 396
- Total Citations
- 94,776
- Citing Countries
- 44
As of June 2026.
Yejin Choi has an h-index of 139 and 94,776 total citations across 628 publications, with research cited by institutions in 44 countries.
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Global Impact Map
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The curious case of neural text degeneration
20195,047
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.
21 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered the analysis of neural text degeneration, establishing a foundational framework for evaluating toxic outputs in language models through highly cited seminal and follow-up work.
The researcher developed Hellaswag, a highly cited benchmark that rigorously evaluates whether machines can genuinely complete sentences, establishing a critical standard for commonsense reasoning in language models.
The researcher developed Winogrande, a large-scale adversarial dataset for evaluating commonsense reasoning in natural language understanding systems.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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