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Chiyuan Zhang

Chiyuan Zhang has 3 papers in our library, in 2015–2025, most of them on Language models & pretraining, Reasoning; often with Bill Yuchen Lin, Bing Xu, Bo Li.

3 papers in our library · 2015–2025

OpenAlex · ORCID · h-index 44 · 160 works · 12,843 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 1,076 times in our library · 8 more in our library without a page yet · ⭐ Paper Stars ranking: #1420

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

🏢GoogleMountain View · United States2018–2026 · 34 papers🎓Massachusetts Institute of TechnologyCambridge · United States2014–2019 · 9 papers🎓Zhejiang UniversityHangzhou · China2010–2018 · 9 papers🔬McGovern Institute for Brain ResearchCambridge · United States2014–2018 · 2 papers🏛️China TobaccoJinrongjie · ChinaEmployment

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).Computer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 1 workHardware and Architecture · 1 workArts and Humanities · 2 worksLanguage and Linguistics · 2 works

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

💡 Concepts 1Key ideas and methods explained on our concept pages.Machine learning · 1 work

 Works by year

2020s

20251 paper20231 paper

2010s

20151 paper

 Papers

The most cited first (OpenAlex and our library).

  1. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems (2015 · 2,000 citations)
    Deep learning developers usually have to pick between two styles. Imperative tensor code is easy to debug and works with host-language tools. Declarative computation graphs expose the whole structure, so the system can optimize it. MXNet tries to give users both styles in one library. Network structure is described…
  2. Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy (2023 · 68 citations)
    Much work on training-data leakage in language models treats memorization as an exact substring match with the training set, and some deployed safeguards, such as GitHub Copilot's option to block suggestions matching public code, rely on this view.
  3. On Memorization of Large Language Models in Logical Reasoning (2025 · 45 citations)
    LLMs score well on hard reasoning benchmarks yet still fail on trivial questions. This raises the suspicion that some benchmark success comes from recalling familiar problems. The authors test this idea in a controlled setting. They define LiMem, a memorization score that multiplies a model's accuracy on a puzzle set…

All works

11 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. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed SystemsarXiv:1512.01274Tianqi Chen, Mu Li, Yutian Li, et al.2015 · 2,000 citations
  2. Understanding deep learning requires rethinking generalization ↗arXiv:1611.03530 · no summary yetChiyuan Zhang et al.2016 · 1,174 citations
  3. Deduplicating Training Data Makes Language Models Better ↗Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetKatherine Lee et al.2022 · 623 citations
  4. A Study on Overfitting in Deep Reinforcement Learning ↗arXiv (Cornell University) · no summary yetChiyuan Zhang et al.2018 · 242 citations
  5. What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation ↗arXiv:2008.03703 · no summary yetVitaly Feldman, Chiyuan Zhang2020 · 193 citations
  6. Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of PrivacyInternational Natural Language Generation Conference (INLG)Daphne Ippolito, Florian Tramer, Milad Nasr, et al.2023 · 68 citations
  7. MUSE: Machine Unlearning Six-Way Evaluation for Language Models ↗arXiv (Cornell University) · no summary yetWeijia Shi et al.2024 · 65 citations
  8. On Memorization of Large Language Models in Logical ReasoningInternational Joint Conference on Natural Language Processing (IJCNLP)Chulin Xie, Yangsibo Huang, Chiyuan Zhang, et al.2025 · 45 citations
  9. MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations ↗arXiv:2502.06453 · no summary yetKaixuan Huang et al.2025 · 42 citations
  10. Evaluating Copyright Takedown Methods for Language Models ↗neural information processing systems · no summary yetBoyi Wei et al.2024 · 25 citations
  11. Fantastic Copyrighted Beasts and How (Not) to Generate Them ↗arXiv (Cornell University) · no summary yetLuxi He et al.2024 · 9 citations

 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 1,076 times (of the 149,439 papers whose bibliographies we read); 164 citing papers with a page here →.

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