Minjoon Seo

2.5k total citations
32 papers, 588 citations indexed

About

Minjoon Seo is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Minjoon Seo has authored 32 papers receiving a total of 588 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 10 papers in Computer Vision and Pattern Recognition and 3 papers in Information Systems. Recurrent topics in Minjoon Seo's work include Topic Modeling (23 papers), Natural Language Processing Techniques (21 papers) and Multimodal Machine Learning Applications (6 papers). Minjoon Seo is often cited by papers focused on Topic Modeling (23 papers), Natural Language Processing Techniques (21 papers) and Multimodal Machine Learning Applications (6 papers). Minjoon Seo collaborates with scholars based in South Korea, Canada and United States. Minjoon Seo's co-authors include Hannaneh Hajishirzi, Ali Farhadi, Jonghyun Choi, Dustin Schwenk, Aniruddha Kembhavi, Robin Jia, Eunsol Choi, Danqi Chen, Adam Fisch and Alon Talmor and has published in prestigious journals such as Transactions of the Association for Computational Linguistics, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Minjoon Seo

28 papers receiving 545 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Minjoon Seo South Korea 10 468 288 69 18 15 32 588
Jungo Kasai United States 13 415 0.9× 139 0.5× 55 0.8× 27 1.5× 10 0.7× 39 538
Panupong Pasupat United States 16 545 1.2× 144 0.5× 79 1.1× 26 1.4× 20 1.3× 21 602
Lemao Liu China 15 721 1.5× 291 1.0× 48 0.7× 25 1.4× 20 1.3× 65 772
Xueguang Ma Canada 10 334 0.7× 99 0.3× 99 1.4× 17 0.9× 12 0.8× 32 427
Anastasia Shimorina France 6 396 0.8× 67 0.2× 50 0.7× 26 1.4× 16 1.1× 11 478
Illia Polosukhin United States 5 1.1k 2.3× 408 1.4× 172 2.5× 26 1.4× 18 1.2× 7 1.1k
Matthew Kelcey United States 2 976 2.1× 379 1.3× 153 2.2× 26 1.4× 16 1.1× 3 1.0k
Haoran Li China 13 590 1.3× 259 0.9× 39 0.6× 23 1.3× 41 2.7× 29 686
Ziqiang Cao China 13 887 1.9× 148 0.5× 85 1.2× 30 1.7× 9 0.6× 38 1.0k

Countries citing papers authored by Minjoon Seo

Since Specialization
Citations

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

Fields of papers citing papers by Minjoon Seo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Minjoon Seo

This figure shows the co-authorship network connecting the top 25 collaborators of Minjoon Seo. A scholar is included among the top collaborators of Minjoon Seo 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 Minjoon Seo. Minjoon Seo 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.
Seo, Minjoon, et al.. (2024). How Well Do Large Language Models Truly Ground?. 2437–2465. 4 indexed citations
3.
4.
Lee, Sangkyu, Sungdong Kim, Ashkan Yousefpour, et al.. (2024). Aligning Large Language Models by On-Policy Self-Judgment. 11442–11459.
5.
Seo, Minjoon, et al.. (2024). KTRL+F: Knowledge-Augmented In-Document Search. 2416–2436. 1 indexed citations
6.
7.
Kim, Sungdong, et al.. (2024). LangBridge: Multilingual Reasoning Without Multilingual Supervision. 7502–7522. 2 indexed citations
8.
Seo, Minjoon, et al.. (2024). Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision. 391–404. 3 indexed citations
9.
Kim, Geewook, et al.. (2024). Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation. 11286–11315. 3 indexed citations
10.
11.
12.
Kim, Jaeyoung, et al.. (2023). Nonparametric Decoding for Generative Retrieval. 12642–12661. 3 indexed citations
13.
Kim, Sungdong, et al.. (2023). Gradient Ascent Post-training Enhances Language Model Generalization. 851–864. 1 indexed citations
14.
15.
Kim, Sungdong, Jamin Shin, Soyoung Kang, et al.. (2023). Aligning Large Language Models through Synthetic Feedback. 13677–13700. 6 indexed citations
16.
Yang, Sohee, et al.. (2023). Knowledge Unlearning for Mitigating Privacy Risks in Language Models. 14389–14408. 20 indexed citations
17.
Lee, Chang-Ho, et al.. (2022). TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models. 6237–6250. 25 indexed citations
18.
Yang, Sohee, et al.. (2022). Generative Multi-hop Retrieval. 1417–1436. 7 indexed citations
19.
Hwang, Wonseok, Jinyeong Yim, Seunghyun Park, Sohee Yang, & Minjoon Seo. (2021). Spatial Dependency Parsing for Semi-Structured Document Information Extraction. 330–343. 32 indexed citations
20.
Seo, Minjoon, et al.. (2015). Solving Geometry Problems: Combining Text and Diagram Interpretation. 1466–1476. 80 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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