Sewon Min

3.6k total citations · 2 hit papers
29 papers, 1.3k citations indexed

About

Sewon Min is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Sewon Min has authored 29 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 5 papers in Information Systems. Recurrent topics in Sewon Min's work include Topic Modeling (22 papers), Natural Language Processing Techniques (18 papers) and Multimodal Machine Learning Applications (7 papers). Sewon Min is often cited by papers focused on Topic Modeling (22 papers), Natural Language Processing Techniques (18 papers) and Multimodal Machine Learning Applications (7 papers). Sewon Min collaborates with scholars based in United States, South Korea and Israel. Sewon Min's co-authors include Hannaneh Hajishirzi, Luke Zettlemoyer, Michael Lewis, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Danqi Chen, Julian Michael and Ludwig Schmidt and has published in prestigious journals such as Transactions of the Association for Computational Linguistics, Journal of Human Resources in Hospitality & Tourism and arXiv (Cornell University).

In The Last Decade

Sewon Min

28 papers receiving 1.2k citations

Hit Papers

Rethinking the Role of Demonstrations: What Makes In-Cont... 2022 2026 2023 2024 2022 2023 100 200 300
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. Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
    2022 383 citations Sewon Min, Xinxi Lyu et al. profile →
  2. Measuring and Narrowing the Compositionality Gap in Language Models
    2023 113 citations Ofir Press, Sewon Min et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sewon Min United States 15 1.1k 347 170 42 38 29 1.3k
Mikel Artetxe Spain 10 1.4k 1.3× 337 1.0× 115 0.7× 32 0.8× 44 1.2× 35 1.6k
Daniel Khashabi United States 20 1.3k 1.2× 432 1.2× 152 0.9× 35 0.8× 74 1.9× 46 1.5k
Sebastian Ruder United States 17 1.3k 1.2× 292 0.8× 142 0.8× 46 1.1× 34 0.9× 46 1.5k
Zhuosheng Zhang China 20 1.3k 1.2× 365 1.1× 125 0.7× 32 0.8× 47 1.2× 73 1.5k
Niket Tandon United States 17 721 0.7× 546 1.6× 103 0.6× 46 1.1× 37 1.0× 51 1.2k
Andrew M. Dai United States 7 1.2k 1.1× 442 1.3× 185 1.1× 54 1.3× 56 1.5× 10 1.4k
Rami Al‐Rfou United States 10 1.3k 1.2× 349 1.0× 114 0.7× 58 1.4× 62 1.6× 18 1.6k
Marjan Ghazvininejad United States 15 1.1k 1.1× 421 1.2× 78 0.5× 51 1.2× 24 0.6× 32 1.3k
Adam Fisch United States 6 1.7k 1.6× 554 1.6× 254 1.5× 28 0.7× 42 1.1× 11 1.8k
Ian Tenney United States 11 791 0.7× 186 0.5× 102 0.6× 32 0.8× 29 0.8× 13 951

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-authorship network of co-authors of Sewon Min

This figure shows the co-authorship network connecting the top 25 collaborators of Sewon Min. A scholar is included among the top collaborators of Sewon Min 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 Sewon Min. Sewon Min 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.
Shi, Weijia, Sewon Min, Michihiro Yasunaga, et al.. (2024). REPLUG: Retrieval-Augmented Black-Box Language Models. 8371–8384. 50 indexed citations
2.
Asai, Akari, Sewon Min, James Grimmelmann, et al.. (2024). CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation. 15134–15158. 1 indexed citations
3.
Min, Sewon, et al.. (2023). CREPE: Open-Domain Question Answering with False Presuppositions. 10457–10480. 7 indexed citations
4.
Min, Sewon, Kalpesh Krishna, Xinxi Lyu, et al.. (2023). FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation. 12076–12100. 80 indexed citations
5.
Press, Ofir, et al.. (2023). Measuring and Narrowing the Compositionality Gap in Language Models. 5687–5711. 113 indexed citations breakdown →
6.
Wang, Boshi, Sewon Min, Xiang Deng, et al.. (2023). Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters. 2717–2739. 49 indexed citations
7.
Wu, Zeqiu, Hao Cheng, Sewon Min, et al.. (2023). InSCIt: Information-Seeking Conversations with Mixed-Initiative Interactions. Transactions of the Association for Computational Linguistics. 11. 453–468. 1 indexed citations
8.
Lyu, Xinxi, Sewon Min, Iz Beltagy, Luke Zettlemoyer, & Hannaneh Hajishirzi. (2023). Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations. 2304–2317. 5 indexed citations
9.
Asai, Akari, Sewon Min, Zexuan Zhong, & Danqi Chen. (2023). Retrieval-based Language Models and Applications. 41–46. 35 indexed citations
10.
Khashabi, Daniel, Xinxi Lyu, Sewon Min, et al.. (2022). Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 3631–3643. 22 indexed citations
11.
Si, Chenglei, Chen Zhao, Sewon Min, & Jordan Boyd‐Graber. (2022). Re-Examining Calibration: The Case of Question Answering. 2814–2829. 3 indexed citations
12.
Min, Sewon, Mike Lewis, Luke Zettlemoyer, & Hannaneh Hajishirzi. (2022). MetaICL: Learning to Learn In Context. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2791–2809. 75 indexed citations
13.
Min, Sewon, Xinxi Lyu, Ari Holtzman, et al.. (2022). Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?. 11048–11064. 383 indexed citations breakdown →
14.
Wortsman, Mitchell, et al.. (2022). Exploring The Landscape of Distributional Robustness for Question Answering Models. 5971–5987. 6 indexed citations
15.
Min, Sewon, Mike Lewis, Hannaneh Hajishirzi, & Luke Zettlemoyer. (2022). Noisy Channel Language Model Prompting for Few-Shot Text Classification. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 5316–5330. 60 indexed citations
16.
Beltagy, Iz, Arman Cohan, Robert K. Logan, Sewon Min, & Sameer Singh. (2022). Zero- and Few-Shot NLP with Pretrained Language Models. 14 indexed citations
17.
Iyer, Srinivasan, Sewon Min, Yashar Mehdad, & Wen-tau Yih. (2021). RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering. 1280–1287. 13 indexed citations
18.
Min, Sewon, Julian Michael, Hannaneh Hajishirzi, & Luke Zettlemoyer. (2020). AmbigQA: Answering Ambiguous Open-domain Questions. 5783–5797. 89 indexed citations
19.
Seo, Min Joon, Sewon Min, Ali Farhadi, & Hannaneh Hajishirzi. (2017). Neural Speed Reading via Skim-RNN.. arXiv (Cornell University). 8 indexed citations
20.
Seo, Min Joon, Sewon Min, Ali Farhadi, & Hannaneh Hajishirzi. (2016). Query-Reduction Networks for Question Answering. International Conference on Learning Representations. 26 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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