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Jingkang Yang

Jingkang Yang has 2 papers in our library, in 2023–2025, most of them on Vision & language, Language models & pretraining; often with Bo Li, Fanyi Pu, Joshua Adrian Cahyono.

2 papers in our library · 2023–2025

OpenAlex · ORCID · h-index 19 · 54 works · 6,596 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 252 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #3789

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

🎓South China University of TechnologyGuangzhou · China2026 · 1 papercurrent🎓Nanyang Technological UniversitySingapore · Singapore2010–2025 · 23 papers🎓Guilin University of Electronic TechnologyGuilin · China2015–2023 · 8 papers🎓Beijing University of Posts and TelecommunicationsBeijing · China2016–2018 · 3 papers🎓Queen Mary University of LondonLondon · United Kingdom2015–2016 · 3 papers🏢SensetimeBeijing · China2020–2023 · 2 papers🎓Rice UniversityHouston · United States2019–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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).Computer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

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

 Works by year

2020s

20251 paperJingkang Yang's papers of 202520231 paperJingkang Yang's papers of 2023

 Papers

The most cited first (OpenAlex and our library).

  1. Otter: A Multi-Modal Model with In-Context Instruction Tuning (2023 · 280 citations)
    Most large multimodal models (LMMs) are tuned to answer one instruction about one image, or at most to draw on earlier dialogue turns.
  2. LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models (2025 · 110 citations)
    Evaluation of large multimodal models (LMMs) is currently fragmented. Each model release tends to use its own scripts for data preparation, answer extraction and scoring, so reported numbers are often not comparable. The authors argue that benchmarks face a trilemma: broad coverage, low cost and freedom from…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Otter: A Multi-Modal Model with In-Context Instruction TuningIEEE Transactions on Pattern Analysis and Machine IntelligenceBo Li, Yuanhan Zhang, Liangyu Chen, et al.2023 · 280 citations
  2. LMMs-Eval: Reality Check on the Evaluation of Large Multimodal ModelsFindings of the Association for Computational LinguisticsKaichen Zhang, Bo Li, Peiyuan Zhang, et al.2025 · 110 citations
  3. Long Context Transfer from Language to Vision ↗arXiv:2406.16852 · no summary yetPeiyuan Zhang et al.2024 · 67 citations

 Co-authors here

8 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 252 times (of the 141,840 papers whose bibliographies we read); 29 citing papers with a page here →.

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