---
title: "Shi Han — author profile and papers — Elamur Research"
description: "Shi Han, Beijing Institute of Technology: 6 papers on Elamur Research (2021–2026), h-index 15 — affiliations, topics, co-authors and who…"
canonical: "https://research.elamur.ai/free/en/authors/shi-han/"
---
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# Shi Han

Shi Han has 6 papers in our library, in 2021–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Question answering](https://md.research.elamur.ai/free/en/topics/question-answering/), [Text generation](https://md.research.elamur.ai/free/en/topics/text-generation/); often with [Dongmei Zhang](https://md.research.elamur.ai/free/en/authors/dongmei-zhang/), [Mengyu Zhou](https://md.research.elamur.ai/free/en/authors/mengyu-zhou/), [Haoyu Dong](https://md.research.elamur.ai/free/en/authors/haoyu-dong/).

6 papers in our library · 2021–2026

[OpenAlex](https://openalex.org/A5100306406) · [ORCID](https://orcid.org/0009-0007-6876-5922) · [**h-index 15**](https://openalex.org/authors/A5100306406) · [139 works](https://openalex.org/works?filter=author.id:A5100306406) · [1,008 citations](https://openalex.org/works?filter=author.id:A5100306406&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 6 works in our library](https://md.research.elamur.ai/free/en/authors/shi-han/#papers) · [🔗 Cited 337 times in our library](https://md.research.elamur.ai/free/en/authors/shi-han/cited-by/) · [3 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #4663](https://md.research.elamur.ai/free/en/paper-stars/page/47/#star-4663)

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Shi+Han) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Shi+Han) · [dblp](https://dblp.org/search?q=Shi+Han) · [ACL Anthology](https://aclanthology.org/search/?q=Shi+Han)

## Affiliations

[🎓**Beijing Institute of Technology** Beijing · China 2022–2026 · 19 papers*current*](https://md.research.elamur.ai/free/en/universities/beijing-institute-of-technology/) · [🎓**Zhejiang University** Hangzhou · China 2004–2026 · 9 papers*current*](https://md.research.elamur.ai/free/en/universities/zhejiang-university/) · [🏢**Huawei Technologies** Shenzhen · China 2023–2026 · 7 papers*current*](https://md.research.elamur.ai/free/en/companies/huawei-technologies/) · [🎓**Tianjin University** Tianjin · China 2018–2026 · 3 papers*current*](https://md.research.elamur.ai/free/en/universities/tianjin-university/) · [🎓**Harbin Engineering University** Harbin · China 2022–2026 · 2 papers*current*](https://md.research.elamur.ai/free/en/universities/harbin-engineering-university/) · [🎓**Chang'an University** Xi'an · China 2026 · 1 paper*current*](https://ror.org/05mxya461) · [🏢**Microsoft** Redmond · United States 2022–2024 · 17 papers](https://md.research.elamur.ai/free/en/companies/microsoft/) · [🏢**Microsoft Research Asia** Beijing · China 2021–2022 · 7 papers](https://md.research.elamur.ai/free/en/companies/microsoft-research-asia/) · [🏢**China Coal Technology and Engineering Group Corp** Beijing · China 2022–2023 · 5 papers](https://ror.org/045d9gj14) · [🎓**Southern University of Science and Technology** Shenzhen · China 2025 · 4 papers](https://md.research.elamur.ai/free/en/universities/southern-university-of-science-and-technology/)

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 7 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 5 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [General Social Sciences · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/general-social-sciences/) · [Social Sciences · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/)

🧭 Topics 6 Research subjects and languages the work is about.[Language models & pretraining · 3 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Evaluation & benchmarks · 1 work](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Question answering · 1 work](https://md.research.elamur.ai/free/en/topics/question-answering/) · [Text generation · 1 work](https://md.research.elamur.ai/free/en/topics/text-generation/) · [Efficient models & compression · 1 work](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/)

## Works by year

### 2020s

[**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/shi-han/year/2026/) · [**2025** 1 paper](https://md.research.elamur.ai/free/en/authors/shi-han/year/2025/) · [**2023** 2 papers](https://md.research.elamur.ai/free/en/authors/shi-han/year/2023/) · [**2021** 1 paper](https://md.research.elamur.ai/free/en/authors/shi-han/year/2021/)

## Papers

The most cited first (OpenAlex and our library).

1. **[Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study](https://md.research.elamur.ai/free/en/papers/sui-2023-table-meets-llm-large-language/)** *(2023 · 235 citations)* Large language models are often given tables flattened into text, but it is unclear whether they actually grasp table structure, and earlier work chose serialization formats in an inconsistent, ad hoc way.
2. **[HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation](https://md.research.elamur.ai/free/en/papers/cheng-2021-hitab-hierarchical-table-dataset-question/)** *(2021 · 108 citations)* Most table reasoning research assumes flat tables. Real statistical and scientific tables, however, often have multi-level headers, unmarked totals and proportions, and implicit links between entities. HiTab is a cross-domain English benchmark built to test question answering (QA) and table-to-text generation (NLG)…
3. **[GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking](https://md.research.elamur.ai/free/en/papers/guo-2023-gpt4graph-large-language-models-understand/)** *(2023 · 75 citations)* Large language models perform well on text, but it is unclear how well they handle graphs, which are central to social network analysis, chemistry, recommendation and knowledge bases.
4. **[TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models](https://md.research.elamur.ai/free/en/papers/he-2025-tablelora-low-rank-adaptation-table/)** *(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:
5. **[Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/liu-2026-not-all-tokens-matter-efficient/)** *(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…
6. **[CAST: Achieving Stable LLM-based Text Analysis for Data Analytics](https://md.research.elamur.ai/free/en/papers/xie-2026-cast-achieving-stable-llm-based/)** *(2026)* When LLMs process free-text columns in tables, their outputs become keys for filtering, grouping and aggregation.

## All works

9 works of theirs in our library, the most cited first; 6 with a page here, the others link their original until we summarise them.

1. **[Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study](https://md.research.elamur.ai/free/en/papers/sui-2023-table-meets-llm-large-language/) [ACM International Conference on Web Search and Data Mining (WSDM)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-web-search-and-data-mining-wsdm/)**Yuan Sui, Mengyu Zhou, Mingjie Zhou, et al.*2023 · 235 citations*
2. [**HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation arXiv:2108.06712**Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, et al.*2021 · 108 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2021-hitab-hierarchical-table-dataset-question/)
3. **[InsightPilot: An LLM-Empowered Automated Data Exploration System ↗](https://aclanthology.org/2023.emnlp-demo.31/) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · no summary yet**Pingchuan Ma et al.*2023 · 86 citations*
4. [**GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking arXiv:2305.15066**Jiayan Guo, Lun Du, Hengyu Liu, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-gpt4graph-large-language-models-understand/)
5. **[Forward-Backward Reasoning in Large Language Models for Mathematical Verification ↗](https://aclanthology.org/2024.findings-acl.397/) Findings of the Association for Computational Linguistics: ACL 2024 · no summary yet**Weisen Jiang et al.*2024 · 44 citations*
6. **[DT-Solver: Automated Theorem Proving with Dynamic-Tree Sampling Guided by Proof-level Value Function ↗](https://aclanthology.org/2023.acl-long.706/) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Haiming Wang et al.*2023 · 40 citations*
7. **[TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models](https://md.research.elamur.ai/free/en/papers/he-2025-tablelora-low-rank-adaptation-table/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyi He, Yihao Liu, Mengyu Zhou, et al.*2025 · 8 citations*
8. **[Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/liu-2026-not-all-tokens-matter-efficient/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hanbing Liu, Lang Cao, Yuanyi Ren, et al.*2026 · 1 citation*
9. **[CAST: Achieving Stable LLM-based Text Analysis for Data Analytics](https://md.research.elamur.ai/free/en/papers/xie-2026-cast-achieving-stable-llm-based/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinxiang Xie, Zihao Li, Wei He, et al.*2026*

## Co-authors here

12 of 18 co-authors with a page, the most papers together first.

- [Dongmei Zhang](https://md.research.elamur.ai/free/en/authors/dongmei-zhang/) ×5
- [Mengyu Zhou](https://md.research.elamur.ai/free/en/authors/mengyu-zhou/) ×4
- [Haoyu Dong](https://md.research.elamur.ai/free/en/authors/haoyu-dong/) ×3
- [Xinyi He](https://md.research.elamur.ai/free/en/authors/xinyi-he/) ×2
- [Hanbing Liu](https://md.research.elamur.ai/free/en/authors/hanbing-liu/)
- [Jian-Guang Lou](https://md.research.elamur.ai/free/en/authors/jian-guang-lou/)
- [Jiaqi Guo](https://md.research.elamur.ai/free/en/authors/jiaqi-guo/)
- [Jiayan Guo](https://md.research.elamur.ai/free/en/authors/jiayan-guo/)
- [Lang Cao](https://md.research.elamur.ai/free/en/authors/lang-cao/)
- [Lun Du](https://md.research.elamur.ai/free/en/authors/lun-du/)
- [Ran Jia](https://md.research.elamur.ai/free/en/authors/ran-jia/)
- [Wei He](https://md.research.elamur.ai/free/en/authors/wei-he/)

## Venues

Where their works here were published, the most works first, with the editions of those that have a page.

- [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×3: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/), [ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)
- [ACM International Conference on Web Search and Data Mining (WSDM)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-web-search-and-data-mining-wsdm/): [WSDM 2023](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-web-search-and-data-mining-wsdm/2023/)
- [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)
- Findings of the Association for Computational Linguistics: ACL 2024
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

## Cited by

Papers of our library cite their works 337 times (of the 149,439 papers whose bibliographies we read); [80 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/shi-han/cited-by/).

Updated 2 November 2026