Ellie Pavlick

7.1k total citations · 4 hit papers
72 papers, 2.9k citations indexed

About

Ellie Pavlick is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Social Psychology. According to data from OpenAlex, Ellie Pavlick has authored 72 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 61 papers in Artificial Intelligence, 16 papers in Computer Vision and Pattern Recognition and 4 papers in Social Psychology. Recurrent topics in Ellie Pavlick's work include Topic Modeling (45 papers), Natural Language Processing Techniques (45 papers) and Multimodal Machine Learning Applications (15 papers). Ellie Pavlick is often cited by papers focused on Topic Modeling (45 papers), Natural Language Processing Techniques (45 papers) and Multimodal Machine Learning Applications (15 papers). Ellie Pavlick collaborates with scholars based in United States, France and Germany. Ellie Pavlick's co-authors include Ian Tenney, Dipanjan Das, Chris Callison-Burch, Tal Linzen, Benjamin Van Durme, Albert Webson, Wei Xu, Quan Ze Chen, Courtney Napoles and Tom Kwiatkowski and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Neuron and PLoS ONE.

In The Last Decade

Ellie Pavlick

65 papers receiving 2.8k citations

Hit Papers

BERT Rediscovers the Clas... 2016 2026 2019 2022 2019 2019 2016 2022 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. BERT Rediscovers the Classical NLP Pipeline
    2019 646 citations Ellie Pavlick et al. profile →
  2. Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
    2019 471 citations Ellie Pavlick, Tal Linzen et al. profile →
  3. Optimizing Statistical Machine Translation for Text Simplification
    2016 298 citations Ellie Pavlick, Chris Callison-Burch et al. profile →
  4. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
    2022 174 citations Teven Le Scao, Angela Fan et al. arXiv (Cornell University) profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ellie Pavlick United States 23 2.5k 540 223 93 89 72 2.9k
Anders Søgaard Denmark 27 2.6k 1.0× 402 0.7× 224 1.0× 110 1.2× 83 0.9× 203 2.9k
Roi Reichart Israel 28 2.5k 1.0× 353 0.7× 194 0.9× 89 1.0× 86 1.0× 108 2.9k
Samuel R. Bowman United States 16 3.2k 1.3× 893 1.7× 267 1.2× 71 0.8× 118 1.3× 40 3.4k
Chris Biemann Germany 27 2.2k 0.9× 181 0.3× 254 1.1× 72 0.8× 50 0.6× 179 2.5k
Slav Petrov United States 27 4.7k 1.9× 821 1.5× 396 1.8× 53 0.6× 77 0.9× 45 5.1k
Robert E. Speer United States 10 2.0k 0.8× 601 1.1× 262 1.2× 38 0.4× 78 0.9× 36 2.3k
Jörg Tiedemann Sweden 32 3.9k 1.5× 622 1.2× 206 0.9× 37 0.4× 61 0.7× 176 4.2k
Kevin Gimpel United States 22 2.4k 0.9× 395 0.7× 302 1.4× 55 0.6× 124 1.4× 81 2.8k
Jean Y. Wu United States 5 3.4k 1.3× 477 0.9× 431 1.9× 54 0.6× 208 2.3× 6 3.8k
Julian Michael United States 11 3.1k 1.2× 1.0k 1.9× 269 1.2× 32 0.3× 87 1.0× 17 3.5k

Countries citing papers authored by Ellie Pavlick

Since Specialization
Citations

This map shows the geographic impact of Ellie Pavlick's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ellie Pavlick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ellie Pavlick more than expected).

Fields of papers citing papers by Ellie Pavlick

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ellie Pavlick. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ellie Pavlick. The network helps show where Ellie Pavlick may publish in the future.

Co-authorship network of co-authors of Ellie Pavlick

This figure shows the co-authorship network connecting the top 25 collaborators of Ellie Pavlick. A scholar is included among the top collaborators of Ellie Pavlick based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ellie Pavlick. Ellie Pavlick is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Millière, Raphaël, et al.. (2025). LLMs as models for analogical reasoning. Journal of Memory and Language. 145. 104676–104676. 2 indexed citations
2.
Serre, T. & Ellie Pavlick. (2025). From prediction to understanding: Will AI foundation models transform brain science?. Neuron. 113(21). 3504–3508. 1 indexed citations
3.
Eickhoff, Carsten, et al.. (2024). Language Models Implement Simple Word2Vec-style Vector Arithmetic. 5030–5047. 1 indexed citations
4.
5.
Pavlick, Ellie, et al.. (2024). Is human compositionality meta-learned?. Behavioral and Brain Sciences. 47. e162–e162. 2 indexed citations
6.
Webson, Albert, et al.. (2023). Are Language Models Worse than Humans at Following Prompts? It’s Complicated. 7662–7686. 4 indexed citations
7.
Pavlick, Ellie, et al.. (2023). Analyzing Modular Approaches for Visual Question Decomposition. 2590–2603. 2 indexed citations
8.
Webson, Albert & Ellie Pavlick. (2022). Do Prompt-Based Models Really Understand the Meaning of Their Prompts?. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2300–2344. 124 indexed citations
9.
Scao, Teven Le, Angela Fan, Christopher Akiki, et al.. (2022). BLOOM: A 176B-Parameter Open-Access Multilingual Language Model. arXiv (Cornell University). 174 indexed citations breakdown →
10.
Kim, Najoung, Ellie Pavlick, Burcu Karagol Ayan, & Deepak Ramachandran. (2021). Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering. 3932–3945. 14 indexed citations
11.
Webson, Albert, Zhizhong Chen, Carsten Eickhoff, & Ellie Pavlick. (2020). Are “Undocumented Workers” the Same as “Illegal Aliens”? Disentangling Denotation and Connotation in Vector Spaces. 4090–4105. 5 indexed citations
12.
Pavlick, Ellie & Tom Kwiatkowski. (2020). Inherent Disagreements in Human Textual Inferences. Meeting of the Association for Computational Linguistics. 2 indexed citations
13.
Berg, Matthew J., et al.. (2020). Grounding Language to Landmarks in Arbitrary Outdoor Environments. 208–215. 12 indexed citations
14.
Pavlick, Ellie, et al.. (2019). Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference. 3428–3448. 471 indexed citations breakdown →
15.
Pavlick, Ellie, et al.. (2019). How well do NLI models capture verb veridicality?. 2230–2240. 13 indexed citations
16.
Poliak, Adam, Rachel Rudinger, J. Edward Hu, et al.. (2018). Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations. arXiv (Cornell University). 4 indexed citations
17.
Cocos, Anne, et al.. (2018). Learning Scalar Adjective Intensity from Paraphrases. SPIRE - Sciences Po Institutional REpository. 1752–1762. 5 indexed citations
18.
Pavlick, Ellie, et al.. (2015). Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015). 26 indexed citations
19.
Pavlick, Ellie, Pushpendre Rastogi, Juri Ganitkevitch, Benjamin Van Durme, & Chris Callison-Burch. (2015). PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification. 425–430. 171 indexed citations
20.
Pavlick, Ellie, et al.. (2015). Adding Semantics to Data-Driven Paraphrasing. 1512–1522. 32 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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