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Yingfa Chen

Yingfa Chen has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Tokenization & segmentation; often with Maosong Sun, Zhiyuan Liu, Biyuan Lin.

3 papers in our library · 2023–2026

OpenAlex · h-index 7 · 20 works · 290 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 16 times in our library · ⭐ Paper Stars ranking: #8384

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

🎓Jiujiang UniversityJiujiang · China2024–2025 · 2 paperscurrent🎓Tsinghua UniversityBeijing · China2021–2024 · 8 papers🏛️Chinese Academy of SciencesBeijing · China2020–2021 · 4 papers🔬Research Center for Eco-Environmental SciencesBeijing · China2020–2021 · 4 papers🔬State Key Joint Laboratory of Environment Simulation and Pollution ControlBeijing · China2020–2021 · 4 papers🎓University of Chinese Academy of SciencesBeijing · China2020–2021 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksLanguage and Linguistics · 3 works

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

 Works by year

2020s

20262 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Sub-Character Tokenization for Chinese Pretrained Language Models (2023 · 29 citations)
    Chinese pretrained language models usually tokenize text either one character at a time or by running sub-word algorithms directly on raw characters.
  2. MiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context Modeling (2026 · 2 citations)
    Full attention in Transformers is costly for very long inputs. Compute grows quadratically with length, and the KV-Cache can reach tens or hundreds of gigabytes at million-token scale. Sparse attention cuts computation but still keeps the whole cache. Linear attention is cheap but compresses context lossily and loses…
  3. StateX: Enhancing RNN Recall via Post-training State Expansion (2026 · 1 citation)
    Recurrent language models such as linear attention and state-space models process long inputs at constant cost per token.

 Co-authors here

12 of 16 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 16 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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