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Zhengtao Yu

Zhengtao Yu has 2 papers in our library, in 2026, most of them on Machine translation, Low-resource languages; often with Hao Peng, Haoran Li, Yuxin Huang.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 37 · 429 works · 5,728 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1 time in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #13241

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

🎓Kunming University of Science and TechnologyKunming · China2005–2026 · 506 paperscurrent🎓Beijing Institute of TechnologyBeijing · China2004–2006 · 5 papers🎓Fuyang Normal UniversityFuyang · China2023–2024 · 4 papers🎓Yunnan Normal UniversityKunming · China2024–2025 · 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 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksSoftware · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Low-resource languages · 1 workMachine translation · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Activation-Guided Local Editing for Jailbreaking Attacks (2026 · 1 citation)
    The paper addresses the weaknesses of current jailbreak techniques used to red-team aligned LLMs. Token-level attacks tend to yield gibberish suffixes that filters catch easily and that rarely transfer to other models. Prompt-level attacks depend on manual crafting or costly iterative search. The authors present…
  2. Layer-aware Dual-directional Modulation for Low-resource Machine Translation (2026)
    LLM-based machine translation still performs much worse for low-resource languages than for high-resource ones, largely because pre-training data is unevenly distributed.

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. Activation-Guided Local Editing for Jailbreaking AttacksAnnual Meeting of the Association for Computational Linguistics (ACL)Jiecong Wang, Haoran Li, Hao Peng, et al.2026 · 1 citation
  2. Layer-aware Dual-directional Modulation for Low-resource Machine TranslationFindings of the Association for Computational LinguisticsSiqi Zhang, Ran Song, Shuting Jiang, et al.2026
  3. MTP-S2UT: Enhancing Speech-to-Speech Translation Quality with Multi-token Prediction ↗arXiv:2510.10003 · no summary yetJianjin Wang et al.2025

 Co-authors here

5 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 1 time (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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