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Mengzhao Jia

Mengzhao Jia has 3 papers in our library, in 2023–2025, most of them on Language models & pretraining, Vision & language, Sentiment & emotion; often with Meng Jiang, Dong-Ho Lee, Francesco Barbieri.

3 papers in our library · 2023–2025

OpenAlex · ORCID · h-index 5 · 33 works · 96 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 53 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7176

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 Notre DameNotre Dame · United States2023–2026 · 18 paperscurrent🎓Shandong UniversityJinan · China2021–2024 · 5 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.

 Fields, topics and concepts

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

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksSocial Sciences · 1 workSociology and Political Science · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 2 worksVision & language · 2 worksMultilingual & cross-lingual NLP · 1 workSentiment & emotion · 1 work

 Works by year

2020s

20251 paper20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning (2024 · 28 citations)
    Large language models handle instructions far better in English than in languages that are underrepresented in their pre-training data.
  2. Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation (2023 · 26 citations)
    The paper addresses Multimodal Sarcasm Explanation (MuSE), where a model writes a short natural-language sentence explaining why an image-plus-caption social media post is sarcastic.
  3. Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench (2025 · 24 citations)
    Large generative models trained on web-scale data can memorize personal details and later reveal them. Machine unlearning has been studied for text-only LLMs, but multimodal models (MLLMs) are harder: knowledge about a person is spread across images and text, so removing it from one modality may leave it recoverable…

All works

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

  1. PLUG: Leveraging Pivot Language in Cross-Lingual Instruction TuningAnnual Meeting of the Association for Computational Linguistics (ACL)Zhihan Zhang, Dong-Ho Lee, Yuwei Fang, et al.2024 · 28 citations
  2. Multi-source Semantic Graph-based Multimodal Sarcasm Explanation GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Liqiang Jing, Xuemeng Song, Kun Ouyang, et al.2023 · 26 citations
  3. Protecting Privacy in Multimodal Large Language Models with MLLMU-BenchConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Zheyuan Liu, Guangyao Dou, Mengzhao Jia, et al.2025 · 24 citations
  4. MMTutorBench: The First Multimodal Benchmark for AI Math Tutoring ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetTengchao Yang et al.2026

 Co-authors here

11 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 53 times (of the 149,439 papers whose bibliographies we read); 12 citing papers with a page here →.

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