Ilia Sucholutsky
- Artificial Intelligence top 10%
- Sociology and Political Science
- Computer Vision and Pattern Recognition
- Experimental and Cognitive Psychology
- Health Informatics top 10%
- Co-authors
- Matthias SchonlauRaja MarjiehSteve RathjeClaire RobertsonDan-Mircea MireaJay Joseph Van BavelThomas L. GriffithsNori Jacoby
- Topics
- Topic Modeling (4 papers)Machine Learning and Data Classification (3 papers)Explainable Artificial Intelligence (XAI) (2 papers)
- Journals
- Proceedings of the National Academy of SciencesScientific ReportsThe Stata Journal Promoting communications on statistics and Stata
- Partner nations
- CanadaUnited StatesUnited Kingdom
In The Last Decade
Ilia Sucholutsky
15 papers receiving 203 citations
Hit Papers
Peers
Comparison fields: 5 of 85
- Artificial Intelligence 103
- Sociology and Political Science 33
- Computer Vision and Pattern Recognition 21
- Experimental and Cognitive Psychology 21
- Health Informatics 20
Countries citing papers authored by Ilia Sucholutsky
This map shows the geographic impact of Ilia Sucholutsky'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 Ilia Sucholutsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ilia Sucholutsky more than expected).
Fields of papers citing papers by Ilia Sucholutsky
This network shows the impact of papers produced by Ilia Sucholutsky. 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 Ilia Sucholutsky. The network helps show where Ilia Sucholutsky may publish in the future.
Co-authorship network of co-authors of Ilia Sucholutsky
This figure shows the co-authorship network connecting the top 25 collaborators of Ilia Sucholutsky. A scholar is included among the top collaborators of Ilia Sucholutsky 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 Ilia Sucholutsky. Ilia Sucholutsky 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 | Explicitly unbiased large language models still form biased associationsbreakdown → | 13 |
| 3 | 7 | |
| 4 | 11 | |
| 5 | 3 | |
| 6 | GPT is an effective tool for multilingual psychological text analysisbreakdown → | 97 |
| 7 | 25 | |
| 8 | 2 | |
| 9 | 7 | |
| 10 | 10 | |
| 11 | 3 | |
| 12 | 2 | |
| 13 | 4 | |
| 14 | 1 | |
| 15 | 7 | |
| 16 | 27 |
About Ilia Sucholutsky
Ilia Sucholutsky is a scholar working on Artificial Intelligence, General Social Sciences and Computer Vision and Pattern Recognition, having authored 16 papers that have together received 219 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Machine Learning and Data Classification (3 papers) and Explainable Artificial Intelligence (XAI) (2 papers). The work is most often cited by research in Health Informatics (20 citations), General Social Sciences (12 citations) and Artificial Intelligence (103 citations). Ilia Sucholutsky has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Matthias Schonlau, Raja Marjieh, Steve Rathje, Claire Robertson, Dan-Mircea Mirea, Jay Joseph Van Bavel, Thomas L. Griffiths, Nori Jacoby, Angelina Wang and Xuechunzi Bai. Their work appears in journals such as Proceedings of the National Academy of Sciences, Scientific Reports and The Stata Journal Promoting communications on statistics and Stata.
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.