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Ling Shi

Ling Shi has 2 papers in our library, in 2026, most of them on Language models & pretraining, Interpretability; often with Bojian Xiong, Deyi Xiong, Hao Wang.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 2 · 11 works · 20 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1 time in our library · ⭐ Paper Stars ranking: #11586

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

🎓Nanyang Technological UniversitySingapore · Singapore2024–2026 · 2 paperscurrent🎓Tianjin UniversityTianjin · China2025–2026 · 2 paperscurrent🎓Chang'an UniversityXi'an · China2026 · 1 papercurrent

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 worksInterpretability · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026 · 1 citation)
    Mainstream LLMs are trained mostly on high-resource languages, so Tibetan remains poorly served despite its millions of speakers and cultural importance. The authors build an end-to-end pipeline to close this gap.
  2. From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models (2026)
    Sparse Autoencoders (SAEs) and other mechanistic interpretability tools can expose meaningful features inside LLMs, but these findings are rarely used to improve the models themselves.

 Co-authors here

12 of 17 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 1 time (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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