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Ji Zhang

Ji Zhang has 5 papers in our library, in 2021–2024, most of them on Language models & pretraining, Vision & language, Reasoning; often with Ming Yan, Anwen Hu, Haiyang Xu.

5 papers in our library · 2021–2024

OpenAlex · ORCID · h-index 6 · 20 works · 262 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 281 times in our library · ⭐ Paper Stars ranking: #3500

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

🎓Southwest Jiaotong UniversityChengdu · China2026 · 2 paperscurrent🏢Alibaba GroupHangzhou · China2021–2025 · 10 papers🏢Alibaba DAMO AcademyHangzhou · China2021–2025 · 7 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 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 5 worksComputer Science · 5 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 5 worksComputer Vision and Pattern Recognition · 3 worksSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 3 worksVision & language · 2 worksReasoning · 1 workBias, fairness & safety · 1 work

 Works by year

2020s

20241 papermPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding20233 papersJi Zhang's papers of 202320211 paperKACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference

 Papers

The most cited first (OpenAlex and our library).

  1. mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration (2023 · 136 citations)
    Multi-modal LLMs usually take one of two routes. Some keep the language model frozen and feed it visual features through a Q-former or a linear projection, which limits how much the modalities can help each other. Others fine-tune the LLM on multi-modal instructions, which tends to weaken its text skills. mPLUG-Owl2…
  2. UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model (2023 · 128 citations)
    Understanding images that contain text, such as documents, tables, charts, webpages and street photos, usually requires either an external OCR system or costly domain-specific pretraining.
  3. mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding (2024 · 109 citations)
    Multimodal LLMs that read document images without an external OCR engine can usually recognize text, but they often miss how that text is organized.
  4. CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility (2023 · 63 citations)
    Existing Chinese LLM benchmarks such as C-EVAL, M3KE and GAOKAO-Bench mostly test knowledge and reasoning.
  5. KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference (2021 · 28 citations)
    Most explanation generators in NLP only justify the chosen label ("why A"). This paper argues that contrastive explanations ("why A and not B") are more useful, both for users and for the model. KACE is a post-hoc framework for natural language inference built from three stages:

 Co-authors here

12 of 18 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 281 times (of the 141,840 papers whose bibliographies we read); 35 citing papers with a page here →.

Updated

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