Daphne Ippolito
- Artificial Intelligence top 5%
- Information Systems top 5%
- Sociology and Political Science
- Computer Vision and Pattern Recognition top 10%
- Health Informatics top 5%
- Co-authors
- Emily ReifAnn YuanAndy CoenenChris Callison-BurchDouglas EckKatherine LeeNicholas CarliniChiyuan Zhang
- Topics
- Topic Modeling (12 papers)Natural Language Processing Techniques (9 papers)Multimodal Machine Learning Applications (4 papers)
- Partner nations
- United StatesSwitzerlandCanada
In The Last Decade
Daphne Ippolito
19 papers receiving 572 citations
Hit Papers
Peers
Comparison fields: 5 of 94
- Artificial Intelligence 355
- Information Systems 152
- Sociology and Political Science 109
- Computer Vision and Pattern Recognition 60
- Health Informatics 44
Countries citing papers authored by Daphne Ippolito
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 11 | |
| 4 | 13 | |
| 5 | 11 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 18 | |
| 9 | 3 | |
| 10 | 14 | |
| 11 | Deduplicating Training Data Makes Language Models Betterbreakdown → | 147 |
| 12 | 2 | |
| 13 | Wordcraft: Story Writing With Large Language Modelsbreakdown → | 174 |
| 14 | 94 | |
| 15 | 31 | |
| 16 | Human and Automatic Detection of Generated Text. | 1 |
| 17 | 23 | |
| 18 | 22 | |
| 19 | 3 | |
| 20 | 19 |
About Daphne Ippolito
Daphne Ippolito is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition, having authored 21 papers that have together received 599 indexed citations. Recurring topics across this work include Topic Modeling (12 papers), Natural Language Processing Techniques (9 papers) and Multimodal Machine Learning Applications (4 papers). The work is most often cited by research in Health Informatics (44 citations), Artificial Intelligence (355 citations) and Information Systems (152 citations). Daphne Ippolito has collaborated with scholars based in United States, Switzerland and Canada. Frequent co-authors include Emily Reif, Ann Yuan, Andy Coenen, Chris Callison-Burch, Douglas Eck, Katherine Lee, Nicholas Carlini, Chiyuan Zhang, Yun William Yu and Yoshua Bengio. Their work appears in journals such as Nature, Journal of the American Medical Informatics Association and Nature Reviews Physics.
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.