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Yang Han

Yang Han has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Evaluation & benchmarks; often with Kai Yu, Lu Chen, Zihan Zhao.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 2 · 7 works · 106 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 57 times in our library · ⭐ Paper Stars ranking: #8074

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

🏛️Chinese Academy of SciencesBeijing · China2023–2025 · 4 paperscurrent🔬Institute of Intelligent MachinesHefei · China2023–2025 · 3 paperscurrent🔬Institute of Computing TechnologyBeijing · China2025 · 1 papercurrent🎓Shanghai Jiao Tong UniversityShanghai · China2022–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workSignal Processing · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksEvaluation & benchmarks · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research (2023 · 91 citations)
    Existing LLM benchmarks that touch on science mostly reuse fixed exam-style objective questions. Such questions may already appear in pre-training data, and they do not test open-ended reasoning. Many of these benchmarks also cover only one discipline. SciEval addresses these gaps with an English benchmark of roughly…
  2. Do We Need Distinct Representations for Every Speech Token? Unveiling and Exploiting Redundancy in Large Speech Language Models (2026 · 2 citations)
    Large Speech Language Models (LSLMs) turn audio into very long token streams, typically 12.5–25 tokens per second. These sequences are much longer than the meaning they carry, which makes inference expensive. The authors test whether every speech token really needs its own representation inside the LLM backbone.

 Co-authors here

3 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 57 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

Updated

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