Ellie Pavlick
- Artificial Intelligence top 0.5%
- Topic Modeling 45
- Natural Language Processing Techniques 45
- Text Readability and Simplification 8
- Explainable Artificial Intelligence (XAI) 7
- Speech and dialogue systems 6
- Domain Adaptation and Few-Shot Learning 6
- Advanced Text Analysis Techniques 4
- Health Informatics top 2%
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- Multimodal Machine Learning Applications 15
- General Social Sciences top 1%
- Co-authors
- Ian TenneyDipanjan DasChris Callison-BurchTal LinzenBenjamin Van DurmeAlbert WebsonQuan Ze ChenWei Xu
- Partner nations
- United StatesFranceGermany
In The Last Decade
Ellie Pavlick
65 papers receiving 2.8k citations
Hit Papers
Peers
Comparison fields: 5 of 124
- Artificial Intelligence 2.5k
- Health Informatics 56
- Computer Vision and Pattern Recognition 540
- General Social Sciences 52
- Computer Science Applications 71
Countries citing papers authored by Ellie Pavlick
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
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
The 25 scholars most cited alongside Ellie Pavlick, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 2 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 2 | |
| 4 | 2025 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 0 | |
| 8 | 2024 | 2 | |
| 9 | 2023 | 4 | |
| 10 | 2023 | 2 | |
| 11 | 2022 | 124 | |
| 12 | BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelbreakdown → | 2022 | 174 |
| 13 | 2021 | 14 | |
| 14 | 2020 | 5 | |
| 15 | Inherent Disagreements in Human Textual Inferences | 2020 | 2 |
| 16 | 2020 | 5 | |
| 17 | Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencebreakdown → | 2019 | 471 |
| 18 | Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations | 2018 | 4 |
| 19 | Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015) | 2015 | 26 |
| 20 | 2015 | 171 |
About Ellie Pavlick
Ellie Pavlick is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Science Applications, having authored 72 papers that have together received 2.9k indexed citations. Recurring topics across this work include Topic Modeling (45 papers), Natural Language Processing Techniques (45 papers), Multimodal Machine Learning Applications (15 papers), Text Readability and Simplification (8 papers), Explainable Artificial Intelligence (XAI) (7 papers), Speech and dialogue systems (6 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Advanced Text Analysis Techniques (4 papers). The work is most often cited by research in Artificial Intelligence (2.5k citations), Health Informatics (56 citations) and Computer Vision and Pattern Recognition (540 citations). Ellie Pavlick has collaborated with scholars based in United States, France and Germany. Frequent co-authors include Ian Tenney, Dipanjan Das, Chris Callison-Burch, Tal Linzen, Benjamin Van Durme, Albert Webson, Quan Ze Chen, Wei Xu, Courtney Napoles and Tom Kwiatkowski.
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.