Sewon Min

3.6k citations
29 papers · 1.3k · 2 hit papers · h-index 15

Impact in

    • Topic Modeling
    • Natural Language Processing Techniques
    • Domain Adaptation and Few-Shot Learning
    • Speech and dialogue systems
    • Advanced Text Analysis Techniques
    • Advanced Graph Neural Networks

Papers in

Journals
Transactions of the Association for Computational Linguistics (1 paper)Journal of Human Resources in Hospitality & Tourism (1 paper)International Conference on Learning Representations (1 paper)Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2 papers)arXiv (Cornell University) (1 paper)

In The Last Decade

Sewon Min

28 papers receiving 1.2k citations

Sewon Min's Hit Papers

Measuring and Narrowing the Compositionality Gap in Language Models 2023 · 113 citations
1130+1+2Years since publication100200300

Peers

Sewon Min
Comparison fields: 5 of 96
  • Artificial Intelligence 1.1k
  • Health Informatics 33
  • Computer Vision and Pattern Recognition 347
  • Information Systems 170
  • Software 16
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Sewon Min relative to Daniel Khashabi United States Daniel Khashabi's profile →
Citations per field
00.5×1.5×2.1×
Daniel Khashabi · 1×
Citations per year

Countries citing papers authored by Sewon Min

Since Specialization
Citations

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

Fields of papers citing papers by Sewon Min

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Sewon Min, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sewon Min Line = papers co-authored together Sewon Min links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
Hit paper breakdown →
2022383
2
Measuring and Narrowing the Compositionality Gap in Language Models
Hit paper breakdown →
2023113
3 202089
4 202380
5 202275
6 201875
7 201971
8 202260
9 201756
10 202450
11 202349
12 202335
13
Query-Reduction Networks for Question Answering
201626
14 202222
15 202214
16 202214
17 202113
18 202313
19 201912
20
Neural Speed Reading via Skim-RNN.
20178

About Sewon Min

Sewon Min is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Sociology and Political Science and Radiology, Nuclear Medicine and Imaging, having authored 29 papers that have together received 1.3k indexed citations. Recurring topics across this work include Topic Modeling (22 papers), Natural Language Processing Techniques (18 papers), Multimodal Machine Learning Applications (7 papers), Domain Adaptation and Few-Shot Learning (5 papers), Advanced Graph Neural Networks (4 papers), Expert finding and Q&A systems (2 papers), Software Engineering Research (2 papers) and Advanced Text Analysis Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (1.1k citations), Health Informatics (33 citations), Computer Vision and Pattern Recognition (347 citations), Information Systems (170 citations) and Software (16 citations). Sewon Min has collaborated with scholars based in United States, South Korea and Israel. Frequent co-authors include Hannaneh Hajishirzi, Luke Zettlemoyer, Michael Lewis, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Danqi Chen, Julian Michael and Ludwig Schmidt. Their work appears in journals such as Transactions of the Association for Computational Linguistics, Journal of Human Resources in Hospitality & Tourism, International Conference on Learning Representations, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) and arXiv (Cornell University).

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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