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Zhao Zhang

Zhao Zhang has 3 papers in our library, in 2017–2026, most of them on Language models & pretraining, Dialogue systems, Speech & spoken language; often with Cho-Jui Hsieh, Feng Zhu, Keqin Chen.

3 papers in our library · 2017–2026

OpenAlex · h-index 0 · 2 works · 0 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 214 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5372

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

🎓Rutgers, The State University of New JerseyNew Brunswick · United States2026 · 1 papercurrent🎓Tianjin UniversityTianjin · China2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

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

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksComputer Vision and Pattern Recognition · 2 worksArtificial Intelligence · 2 worksHardware and Architecture · 1 workArts and Humanities · 1 workExperimental and Cognitive Psychology · 1 workLanguage and Linguistics · 1 workPsychology · 1 work

🧭 Topics 4Research subjects and languages the work is about.Dialogue systems · 1 workLanguage models & pretraining · 1 workVision & language · 1 workSpeech & spoken language · 1 work

 Works by year

2020s

20261 paper20231 paper

2010s

20171 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic (2023 · 210 citations)
    Current multimodal LLMs can describe what an image contains, but they cannot handle where things are. A user cannot point to a region, and the model cannot point back to the objects it mentions. The authors call this missing ability Referential Dialogue (RD) and present Shikra, a generalist MLLM designed for it.
  2. ImageNet Training in Minutes (2017 · 112 citations)
    Training a standard classifier on ImageNet-1k once required days or weeks: 90 epochs of ResNet-50 on one NVIDIA M40 took about 14 days.
  3. PED: Route-Decoupled Diagnostics for Persona Consistency in Spoken Agents (2026)
    Spoken role-playing agents often slip out of character over long conversations. Existing benchmarks usually give a single holistic "voice + character" score, which does not show whether the fault comes from the generated words or from their acoustic delivery. The authors propose PED (Persona–Emotion Decoupling), a…

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. Shikra: Unleashing Multimodal LLM's Referential Dialogue MagicarXiv:2306.15195Keqin Chen, Zhao Zhang, Weili Zeng, et al.2023 · 210 citations
  2. ImageNet Training in MinutesarXiv:1709.05011Yang You, Zhao Zhang, Cho-Jui Hsieh, et al.2017 · 112 citations
  3. PED: Route-Decoupled Diagnostics for Persona Consistency in Spoken AgentsFindings of the Association for Computational LinguisticsWeihao Liu, Junrui Wei, Zhao Zhang, et al.2026
  4. Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetCan Jin 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.

 Cited by

Papers of our library cite their works 214 times (of the 149,439 papers whose bibliographies we read); 53 citing papers with a page here →.

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