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Suyu Ge

Suyu Ge has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, Bias, fairness & safety, Efficient models & compression; often with Jiawei Han, Chunting Zhou, Jianfeng Gao.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 11 · 25 works · 746 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 242 times in our library · ⭐ Paper Stars ranking: #4306

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

🎓University of Illinois Urbana-ChampaignUrbana · United States2022–2026 · 8 paperscurrent🎓Tsinghua UniversityBeijing · China2019–2021 · 10 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.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksEfficient models & compression · 1 workBias, fairness & safety · 1 work

 Works by year

2020s

20241 paperMART: Improving LLM Safety with Multi-round Automatic Red-Teaming20231 paperModel Tells You What to Discard: Adaptive KV Cache Compression for LLMs

 Papers

The most cited first (OpenAlex and our library).

  1. Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs (2023 · 198 citations)
    Large language models cache the key and value vectors of every previous token during generation. This KV cache grows with model size and output length and often exceeds GPU memory. Offloading it to CPU or NVMe slows inference, and most token-pruning methods require retraining. The authors propose FastGen, a…
  2. MART: Improving LLM Safety with Multi-round Automatic Red-Teaming (2024 · 79 citations)
    Making LLMs safe usually depends on human red-teaming, where many annotators spend months writing attack prompts and safe answers.

 Co-authors here

10 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 242 times (of the 141,840 papers whose bibliographies we read); 17 citing papers with a page here →.

Updated

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