Jun Yu
Jun Yu has 4 papers in our library, in 2019–2026, most of them on Bias, fairness & safety, Named entity recognition, Language models & pretraining; often with Min Zhang, Daojing He, Jing Li.
OpenAlex · ORCID · Wikidata · h-index 53 · 392 works · 14,044 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 194 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #4182
Affiliations
Fields, topics and concepts
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksComputer Vision and Pattern Recognition · 2 worksArtificial Intelligence · 3 worksArts and Humanities · 3 worksLanguage and Linguistics · 3 works
🧭 Topics 5Research subjects and languages the work is about.Question answering · 1 workBias, fairness & safety · 2 worksLanguage models & pretraining · 1 workNamed entity recognition · 1 workReasoning · 1 work
Works by year
2020s
2010s
Papers
- ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering (2019 · 403 citations)
Video question answering (VideoQA) has had weaker benchmarks than image QA. Existing datasets tend to be small, rely on QA pairs generated automatically from captions, use very short clips, or cover only a few activity types. To address this, the authors built ActivityNet-QA, a fully human-annotated, open-ended… - MTSA: Multi-turn Safety Alignment for LLMs through Multi-round Red-teaming (2025 · 14 citations)
Jailbreak research and safety training have mostly targeted single-turn prompts. In multi-turn chats, an attacker can spread harmful intent across several innocent-looking turns, and LLMs are more likely to give in. MTSA is a two-stage framework for multi-turn safety. - Safety Alignment via Constrained Knowledge Unlearning (2025 · 13 citations)
Aligned large language models still produce harmful content under jailbreak attacks. The authors argue this happens because standard defenses, such as continued safety training, teach models to refuse without removing the dangerous knowledge itself, and they can also erode general skills. They propose Constrained… - Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition (2026)
Grounded Multimodal Named Entity Recognition (GMNER) asks a system to find named entities in a sentence, label their types, and link each one to a bounding box in the paired image, or mark it as absent from the image.
All works
- ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question AnsweringProceedings of the AAAI Conference on Artificial IntelligenceZhou Yu, Dejing Xu, Jun Yu, et al.2019 · 403 citations
- Prompting Large Language Models with Answer Heuristics for Knowledge-Based Visual Question Answering ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetZhenwei Shao et al.2023 · 262 citations
- Deep Graph-neighbor Coherence Preserving Network for Unsupervised Cross-modal Hashing ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetJun Yu et al.2021 · 199 citations
- MTSA: Multi-turn Safety Alignment for LLMs through Multi-round Red-teamingAnnual Meeting of the Association for Computational Linguistics (ACL)Weiyang Guo, Jing Li, Wenya Wang, et al.2025 · 14 citations
- Safety Alignment via Constrained Knowledge UnlearningAnnual Meeting of the Association for Computational Linguistics (ACL)Zesheng Shi, Yucheng Zhou, Jing Li, et al.2025 · 13 citations
- Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity RecognitionFindings of the Association for Computational LinguisticsJinlong Ma, Yu Zhang, Xuefeng Bai, et al.2026
Co-authors here
- Min Zhang ×3
- Daojing He ×2
- Jing Li ×2
- Yu Li ×2
- Dacheng Tao
- Fangming Liu
- Kehai Chen
- Weiyang Guo
- Wenya Wang
- Xuefeng Bai
- Yu Zhang
- Yucheng Zhou
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2025 ×2
- Proceedings of the AAAI Conference on Artificial Intelligence ×2: Proceedings of the AAAI Conference on Artificial Intelligence 2019
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
Cited by
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