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Yingyan Celine Lin

Yingyan Celine Lin has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining; often with Yonggan Fu, Shizhe Diao, Xin Dong.

3 papers in our library · 2024–2026

OpenAlex · ORCID · Wikidata · h-index 28 · 153 works · 2,572 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 123 times in our library · ⭐ Paper Stars ranking: #7220

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

🎓Georgia Institute of TechnologyAtlanta · United States2022–2026 · 43 paperscurrent🎓Rice UniversityHouston · United States2018–2023 · 68 papers🎓University of Illinois Urbana-ChampaignUrbana · United States2011–2021 · 7 papers🎓Huazhong University of Science and TechnologyWuhan · China2009–2011 · 5 papers🎓National Central UniversityTaoyuan City · Taiwan2003–2008 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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 · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

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

 Works by year

2020s

20261 paper20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Small Language Models are the Future of Agentic AI (2025 · 92 citations)
    This position paper from NVIDIA Research and Georgia Tech challenges a common industry practice: sending every request an AI agent makes to one large, general-purpose LLM hosted in the cloud.
  2. Hymba: A Hybrid-head Architecture for Small Language Models (2024 · 50 citations)
    Small language models must balance accuracy against memory and speed. Transformers recall context well, but their compute cost and KV cache grow quickly with sequence length. State space models such as Mamba run cheaply but recall poorly. Earlier hybrids stacked the two layer types one after another, so a weak layer…
  3. Think Hard Only When Needed: A Hybrid Best-of-N and Beam Search for Efficient Test-Time Compute (2026)
    Test-time compute lets small language models reason better by spending extra inference effort, but the two standard strategies have opposite costs.

 Co-authors here

12 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 123 times (of the 149,439 papers whose bibliographies we read); 23 citing papers with a page here →.

Updated

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