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Shi Han

Shi Han has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning, Efficient models & compression; often with Dongmei Zhang, Haoyu Dong, Mengyu Zhou.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 15 · 139 works · 1,008 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 190 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #10071

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

🎓Beijing Institute of TechnologyBeijing · China2022–2026 · 19 paperscurrent🎓Zhejiang UniversityHangzhou · China2004–2026 · 9 paperscurrent🏢Huawei TechnologiesShenzhen · China2023–2026 · 7 paperscurrent🎓Tianjin UniversityTianjin · China2018–2026 · 3 paperscurrent🎓Harbin Engineering UniversityHarbin · China2022–2026 · 2 paperscurrent🎓Chang'an UniversityXi'an · China2026 · 1 papercurrent🏢MicrosoftRedmond · United States2022–2024 · 17 papers🏢Microsoft Research AsiaBeijing · China2021–2022 · 7 papers🏢China Coal Technology and Engineering Group CorpBeijing · China2022–2023 · 5 papers🎓Southern University of Science and TechnologyShenzhen · China2025 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 36 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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksEfficient models & compression · 1 workReasoning · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models (2025 · 8 citations)
    Large language models often misread tables when tuned with parameter-efficient methods such as LoRA. Flattening a grid into a token sequence hides which cells share a row or column, and nested headers become hard to interpret. TableLoRA is a PEFT-compatible module that addresses these problems with two components:
  2. Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning (2026 · 1 citation)
    Reasoning LLMs often generate chains of thought much longer than they need to be. Earlier RL methods for shortening them apply length penalties uniformly across tokens, which can hurt answer correctness. This paper treats the problem at the token level. It notes that a large share of CoT tokens barely influence the…

All works

6 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. HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation ↗arXiv:2108.06712 · no summary yetZhoujun Cheng et al.2021 · 108 citations
  2. InsightPilot: An LLM-Empowered Automated Data Exploration System ↗Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · no summary yetPingchuan Ma et al.2023 · 86 citations
  3. Forward-Backward Reasoning in Large Language Models for Mathematical Verification ↗Findings of the Association for Computational Linguistics: ACL 2024 · no summary yetWeisen Jiang et al.2024 · 44 citations
  4. DT-Solver: Automated Theorem Proving with Dynamic-Tree Sampling Guided by Proof-level Value Function ↗Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetHaiming Wang et al.2023 · 40 citations
  5. TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Xinyi He, Yihao Liu, Mengyu Zhou, et al.2025 · 8 citations
  6. Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Hanbing Liu, Lang Cao, Yuanyi Ren, et al.2026 · 1 citation

 Co-authors here

6 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 190 times (of the 149,439 papers whose bibliographies we read); 39 citing papers with a page here →.

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