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Dengpan Ye

Dengpan Ye has 2 papers in our library, in 2026, most of them on Evaluation & benchmarks, Code & programming languages, Bias, fairness & safety; often with Chi Zhang, Fei Li, Guotong Geng.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 22 · 125 works · 1,693 citations OpenAlex, 2026-10-14

📄 2 works in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #13510

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

🎓Wuhan UniversityWuhan · China2007–2026 · 100 paperscurrent🎓Guangzhou UniversityGuangzhou · China2025–2026 · 8 papersEmploymentcurrent🎓Nanjing University of Science and TechnologyNanjing · China2005–2006 · 4 papers🎓Singapore Management UniversitySingapore · Singapore2011–2012 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 1 workArts and Humanities · 1 workComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 workLanguage and Linguistics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Bias, fairness & safety · 1 workCode & programming languages · 1 workEvaluation & benchmarks · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Omni-I2C: A Holistic Benchmark for High-Fidelity Image-to-Code Generation (2026)
    Omni-I2C is a benchmark that tests whether Large Multimodal Models (LMMs) can turn structured digital graphics into executable code that reproduces the original image.
  2. SHARP: Self-adaptive Harmful Category-aware Prompt Generation for Black-box Jailbreaking (2026)
    Jailbreak methods used in red-teaming usually treat all harmful requests the same way. The authors show this is a weakness. Across four LLMs, existing prompts succeed very unevenly depending on the request type. Explicit categories such as sexual content are blocked more often than subtle ones such as disinformation.…

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. Implicit Identity Driven Deepfake Face Swapping Detection ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetBaojin Huang et al.2023 · 172 citations
  2. Omni-I2C: A Holistic Benchmark for High-Fidelity Image-to-Code GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Jiawei Zhou, Chi Zhang, Xiang Feng, et al.2026
  3. SHARP: Self-adaptive Harmful Category-aware Prompt Generation for Black-box JailbreakingAnnual Meeting of the Association for Computational Linguistics (ACL)Yingjie Xue, Xingyou Xia, Jun Zhang, et al.2026

 Co-authors here

7 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.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/dengpan-ye/