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Zhanhui Zhou

Zhanhui Zhou has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Machine translation, Dialogue systems; often with Jie Liu, Bo Zheng, Chao Yang.

3 papers in our library · 2024–2026

OpenAlex · h-index 4 · 14 works · 120 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 134 times in our library · ⭐ Paper Stars ranking: #6534

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 California, BerkeleyBerkeley · United States2026 · 1 papercurrent🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2024 · 8 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksInformation Systems · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 3 worksDialogue systems · 1 workEvaluation & benchmarks · 1 workMachine translation · 1 work

 Works by year

2020s

20261 paper20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues (2024 · 174 citations)
    Existing LLM benchmarks mostly test single-turn instructions. Multi-turn ones such as MT-Bench cover only two turns and broad skill categories, so they miss much of what happens in real chatbot conversations. MT-Bench-101 fills this gap with a fine-grained multi-turn benchmark. The authors studied real user–chatbot…
  2. Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models (2024 · 17 citations)
    Aligning very large language models with human preferences through fine-tuning is costly and hard to engineer.
  3. dLLM: Simple Diffusion Language Modeling (2026 · 3 citations)
    Diffusion language models (DLMs) are a growing alternative to autoregressive generation, but each open model tends to ship with its own one-off code.

 Co-authors here

12 of 16 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 134 times (of the 149,439 papers whose bibliographies we read); 35 citing papers with a page here →.

Updated

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