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Urvashi Khandelwal

Urvashi Khandelwal has 3 papers in our library, in 2018–2022, most of them on Language models & pretraining, Vision & language; often with Christopher D. Manning, Dan Jurafsky, Fangyu Liu.

3 papers in our library · 2018–2022

OpenAlex · h-index 14 · 16 works · 4,361 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 4,013 times in our library · 6 more in our library without a page yet · ⭐ Paper Stars ranking: #3045

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Stanford UniversityStanford · United States2018–2024 · 9 paperscurrent🎓University of Illinois Urbana-ChampaignUrbana · United States2013–2014 · 3 papers🏢MetaMenlo Park · United States2019–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksVision & language · 1 work

 Works by year

2020s

20221 paper

2010s

20191 paper20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context (2018 · 399 citations)
    Neural language models beat n-gram models, and this is often credited to their use of distant context. This paper tests that claim directly. It asks three things: how many preceding tokens an LSTM language model actually exploits, whether near and far context are represented differently, and how a copy mechanism…
  2. BAM! Born-Again Multi-Task Networks for Natural Language Understanding (2019 · 285 citations)
    Multi-task neural models in NLP often fail to match the accuracy of separately trained single-task models.
  3. Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding (2022 · 158 citations)
    Pix2Struct is a general pretrained model for images where text and visuals are mixed, such as documents, charts, diagrams, app screens, and photos containing text.

All works

9 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. What Does BERT Look at? An Analysis of BERT's Attention ↗Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · no summary yetKevin Clark et al.2019 · 2,049 citations
  2. Generalization through Memorization: Nearest Neighbor Language Models ↗arXiv:1911.00172 · no summary yetUrvashi Khandelwal et al.2019 · 464 citations
  3. Emergent linguistic structure in artificial neural networks trained by self-supervision ↗Proceedings of the National Academy of Sciences · no summary yetChristopher D. Manning et al.2020 · 461 citations
  4. Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use ContextAnnual Meeting of the Association for Computational Linguistics (ACL)Urvashi Khandelwal, He He, Peng Qi, et al.2018 · 399 citations
  5. BAM! Born-Again Multi-Task Networks for Natural Language UnderstandingAnnual Meeting of the Association for Computational Linguistics (ACL)Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, et al.2019 · 285 citations
  6. Nearest Neighbor Machine Translation ↗arXiv (Cornell University) · no summary yetUrvashi Khandelwal et al.2020 · 193 citations
  7. With Little Power Comes Great Responsibility ↗Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yetDallas Card et al.2020 · 173 citations
  8. Pix2Struct: Screenshot Parsing as Pretraining for Visual Language UnderstandingarXiv:2210.03347Kenton Lee, Mandar Joshi, Iulia Turc, et al.2022 · 158 citations
  9. Sample Efficient Text Summarization Using a Single Pre-Trained Transformer ↗arXiv (Cornell University) · no summary yetUrvashi Khandelwal et al.2019 · 81 citations

 Co-authors here

12 of 16 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 4,013 times (of the 149,439 papers whose bibliographies we read); 851 citing papers with a page here →.

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