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Sangwoo Cho

Sangwoo Cho has 2 papers in our library, in 2024, most of them on Language models & pretraining, Vision & language; often with Dong Yu, Kaiqiang Song, Wenlin Yao.

2 papers in our library · 2024

OpenAlex · h-index 10 · 35 works · 456 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 165 times in our library · ⭐ Paper Stars ranking: #4585

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of Central FloridaOrlando · United States2019–2023 · 7 papers🏢TencentShenzhen · China2022–2024 · 4 papers🎓Chungnam National UniversityDaejeon · South Korea2022–2023 · 2 papers🎓University of Southern CaliforniaLos Angeles · United States2022 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksVision & language · 1 work

 Works by year

2020s

20242 papersSangwoo Cho's papers of 2024

 Papers

The most cited first (OpenAlex and our library).

  1. InFoBench: Evaluating Instruction Following Ability in Large Language Models (2024 · 133 citations)
    Judging whether large language models obey user instructions usually relies on pairwise A/B comparisons, Elo ratings or a single holistic score.
  2. MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning (2024 · 83 citations)
    General-purpose open-source large multimodal models (LMMs) handle natural photos well but struggle with charts. Charts encode data through abstract elements such as axes, legends and trend lines, and these models get little training on them. The authors address this with three contributions.

 Co-authors here

9 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 165 times (of the 141,840 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

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