Daphne Ippolito

5.1k total citations · 2 hit papers
21 papers, 599 citations indexed

About

Daphne Ippolito is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Daphne Ippolito has authored 21 papers receiving a total of 599 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 5 papers in Information Systems and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Daphne Ippolito's work include Topic Modeling (12 papers), Natural Language Processing Techniques (9 papers) and Multimodal Machine Learning Applications (4 papers). Daphne Ippolito is often cited by papers focused on Topic Modeling (12 papers), Natural Language Processing Techniques (9 papers) and Multimodal Machine Learning Applications (4 papers). Daphne Ippolito collaborates with scholars based in United States, Switzerland and Canada. Daphne Ippolito's co-authors include Emily Reif, Andy Coenen, Ann Yuan, Chris Callison-Burch, Douglas Eck, Katherine Lee, Nicholas Carlini, Chiyuan Zhang, Yun William Yu and Abhinav Sharma and has published in prestigious journals such as Nature, Journal of the American Medical Informatics Association and Nature Reviews Physics.

In The Last Decade

Daphne Ippolito

19 papers receiving 572 citations

Hit Papers

Wordcraft: Story Writing With Large Language Models 2022 2026 2023 2024 2022 2022 50 100 150
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. Wordcraft: Story Writing With Large Language Models
    2022 174 citations Ann Yuan, Andy Coenen et al. profile →
  2. Deduplicating Training Data Makes Language Models Better
    2022 147 citations Katherine Lee, Daphne Ippolito et al. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daphne Ippolito United States 12 355 152 109 60 44 21 599
Chen Luo China 11 341 1.0× 101 0.7× 254 2.3× 33 0.6× 4 0.1× 36 731
Stefano Cirillo Italy 11 144 0.4× 65 0.4× 71 0.7× 35 0.6× 43 1.0× 49 381
Christoph Lofi Netherlands 13 208 0.6× 73 0.5× 38 0.3× 42 0.7× 15 0.3× 46 472
Vive Kumar Canada 14 219 0.6× 155 1.0× 71 0.7× 26 0.4× 23 0.5× 80 718
Ziang Xiao United States 12 234 0.7× 42 0.3× 112 1.0× 47 0.8× 31 0.7× 30 461
Karën Fort France 8 263 0.7× 100 0.7× 75 0.7× 74 1.2× 7 0.2× 31 605
Pranay Lohia India 6 354 1.0× 84 0.6× 58 0.5× 40 0.7× 96 2.2× 13 650
Seda Gürses Belgium 15 363 1.0× 424 2.8× 410 3.8× 30 0.5× 5 0.1× 33 806
Lefteris Moussiades Greece 7 489 1.4× 140 0.9× 81 0.7× 26 0.4× 107 2.4× 20 724
Faten Kharbat United Arab Emirates 10 128 0.4× 110 0.7× 31 0.3× 52 0.9× 17 0.4× 43 530

Countries citing papers authored by Daphne Ippolito

Since Specialization
Citations

This map shows the geographic impact of Daphne Ippolito'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 Daphne Ippolito with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daphne Ippolito more than expected).

Fields of papers citing papers by Daphne Ippolito

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daphne Ippolito. 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 Daphne Ippolito. The network helps show where Daphne Ippolito may publish in the future.

Co-authorship network of co-authors of Daphne Ippolito

This figure shows the co-authorship network connecting the top 25 collaborators of Daphne Ippolito. A scholar is included among the top collaborators of Daphne Ippolito 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 Daphne Ippolito. Daphne Ippolito 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.
Ippolito, Daphne & Yiming Zhang. (2025). AI can learn to show its workings through trial and error. Nature. 645(8081). 594–595.
2.
Dugan, Liam, et al.. (2024). RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors. 12463–12492. 3 indexed citations
3.
Longpre, Shayne, Emily Reif, Katherine Lee, et al.. (2024). A Pretrainer’s Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity. 3245–3276. 11 indexed citations
4.
Dihal, Kanta, Katy Ilonka Gero, Daphne Ippolito, et al.. (2024). Generative AI and science communication in the physical sciences. Nature Reviews Physics. 6(3). 162–165. 13 indexed citations
5.
Ippolito, Daphne, Florian Tramèr, Milad Nasr, et al.. (2023). Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy. Repository for Publications and Research Data (ETH Zurich). 28–53. 11 indexed citations
6.
Ippolito, Daphne, et al.. (2023). Are aligned neural networks adversarially aligned?. 61478–61500.
7.
Ippolito, Daphne, Nicholas Carlini, Katherine Lee, Milad Nasr, & Yun William Yu. (2023). Reverse-Engineering Decoding Strategies Given Blackbox Access to a Language Generation System. 396–406. 1 indexed citations
8.
Dugan, Liam, et al.. (2023). Real or Fake Text?: Investigating Human Ability to Detect Boundaries between Human-Written and Machine-Generated Text. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 12763–12771. 18 indexed citations
9.
Lee, Katherine, A. Feder Cooper, James Grimmelmann, & Daphne Ippolito. (2023). AI and Law: The Next Generation. SSRN Electronic Journal. 3 indexed citations
10.
Callison-Burch, Chris, et al.. (2022). Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence. 9379–9393. 14 indexed citations
11.
Lee, Katherine, Daphne Ippolito, Chiyuan Zhang, et al.. (2022). Deduplicating Training Data Makes Language Models Better. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 8424–8445. 147 indexed citations breakdown →
12.
Ippolito, Daphne, Liam Dugan, Emily Reif, et al.. (2022). The Case for a Single Model that can Both Generate Continuations and Fill-in-the-Blank. 2421–2432. 2 indexed citations
13.
Yuan, Ann, Andy Coenen, Emily Reif, & Daphne Ippolito. (2022). Wordcraft: Story Writing With Large Language Models. 841–852. 174 indexed citations breakdown →
14.
Bengio, Yoshua, et al.. (2020). The need for privacy with public digital contact tracing during the COVID-19 pandemic. The Lancet Digital Health. 2(7). e342–e344. 94 indexed citations
15.
Bengio, Yoshua, et al.. (2020). Inherent privacy limitations of decentralized contact tracing apps. Journal of the American Medical Informatics Association. 28(1). 193–195. 31 indexed citations
16.
Ippolito, Daphne, Daniel Duckworth, Chris Callison-Burch, & Douglas Eck. (2019). Human and Automatic Detection of Generated Text.. arXiv (Cornell University). 1 indexed citations
17.
Sedoc, João, et al.. (2019). . 60–65. 23 indexed citations
18.
Ippolito, Daphne, David Grangier, Chris Callison-Burch, & Douglas Eck. (2019). Unsupervised Hierarchical Story Infilling. 37–43. 22 indexed citations
19.
Sedoc, João, et al.. (2018). ChatEval: A Tool for the Systematic Evaluation of Chatbots. 42–44. 3 indexed citations
20.
Hewitt, John, et al.. (2018). Learning Translations via Images with a Massively Multilingual Image Dataset. OpenBU/Boston University Institutional Repository (Boston University). 2566–2576. 19 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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