Elamur Research⌕
☰
home / authors / Shiguo Lian

Shiguo Lian

Shiguo Lian has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining, English; often with Kai Wang, Ning Wang, Zhaoxiang Liu.

2 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 42 · 326 works · 7,415 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 58 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #11260

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

🏢China United Network Communications GroupBeijing · China2021–2026 · 55 paperscurrent🏢OrangeParis · France2005–2015 · 127 papers🎓Nanjing University of Science and TechnologyNanjing · China2003–2011 · 26 papers🏢Huawei TechnologiesShenzhen · China2011–2020 · 24 papers🎓Institute of Electrical and Electronics EngineersPiscataway · United States2010–2011 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Methodology of Adapting Large English Language Models for Specific Cultural Contexts (2024 · 2 citations)
    Leading LLMs are trained mostly on English, so they lack local knowledge and can clash with local values when used in another culture.
  2. Mixture of Heterogeneous Grouped Experts for Language Modeling (2026)
    Standard Mixture-of-Experts (MoE) language models use experts of one fixed size, so every token receives the same compute regardless of how hard it is to predict.

All works

3 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. DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models ↗arXiv:2503.04472 · no summary yetYi Shen et al.2025 · 57 citations
  2. Methodology of Adapting Large English Language Models for Specific Cultural ContextsarXiv:2406.18192Wenjing Zhang, Siqi Xiao, Xuejiao Lei, et al.2024 · 2 citations
  3. Mixture of Heterogeneous Grouped Experts for Language ModelingAnnual Meeting of the Association for Computational Linguistics (ACL)Zhicheng Ma, Xiang Liu, Zhaoxiang Liu, et al.2026

 Co-authors here

5 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 58 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/shiguo-lian/