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Yiran Zhong

Yiran Zhong has 3 papers in our library, in 2022–2024, most of them on Language models & pretraining; often with Weixuan Sun, Zhen Qin, Dong Li.

3 papers in our library · 2022–2024

OpenAlex · ORCID · Wikidata · h-index 25 · 92 works · 2,513 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 221 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6426

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

🏢Alibaba GroupHangzhou · China2026 · 2 paperscurrent🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2022–2025 · 27 papers🎓Australian National UniversityCanberra · Australia2014–2021 · 17 papersEducation🏢SensetimeBeijing · China2021–2022 · 9 papers🏛️Commonwealth Scientific and Industrial Research OrganisationCanberra · Australia2018–2019 · 5 papers🔬Data61Sydney · Australia2018–2019 · 4 papers🎓China University of Mining and TechnologyXuzhou · China2024 · 2 papers🔬Australian Centre for Robotic VisionBrisbane · Australia2018 · 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 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 · 2 worksHardware and Architecture · 1 work

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

 Works by year

2020s

20241 paper20231 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. The Devil in Linear Transformer (2022 · 67 citations)
    Kernel-based linear transformers reduce attention cost from quadratic to linear in sequence length, but they usually lose accuracy compared with the standard softmax transformer.
  2. TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer (2023 · 33 citations)
    Linear attention promises to remove the quadratic cost of softmax attention. In practice, earlier linear-attention models have trailed Transformers in language-modeling quality and rarely ran faster on real hardware. The authors close this gap with TransNormerLLM, a redesigned version of their TransNormer…
  3. Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models (2024 · 31 citations)
    Linear attention should cost O(n) instead of the O(n²) of softmax attention. In causal language modeling, however, existing implementations lose this advantage: the kernel trick needs a cumulative summation (cumsum) that parallelizes poorly on GPUs. Lightning Attention-1 had already reduced the memory-I/O bottleneck…

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. The Devil in Linear TransformerConference on Empirical Methods in Natural Language Processing (EMNLP)Zhen Qin, Xiaodong Han, Weixuan Sun, et al.2022 · 67 citations
  2. TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormerarXiv:2307.14995Zhen Qin, Dong Li, Weigao Sun, et al.2023 · 33 citations
  3. Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language ModelsarXiv:2401.04658Zhen Qin, Weigao Sun, Dong Li, et al.2024 · 31 citations
  4. Linear Attention Sequence Parallelism ↗arXiv:2404.02882 · no summary yetWeigao Sun et al.2024 · 12 citations

 Co-authors here

11 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 221 times (of the 149,439 papers whose bibliographies we read); 47 citing papers with a page here →.

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