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Yihe Deng

Yihe Deng has 2 papers in our library, in 2019–2023, most of them on Language models & pretraining; often with Kun He, Shuhuai Ren, Wanxiang Che.

2 papers in our library · 2019–2023

OpenAlex · ORCID · h-index 5 · 14 works · 785 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 328 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #2822

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 California, Los AngelesLos Angeles · United States2019–2025 · 9 paperscurrent🎓Wuhan Institute of TechnologyWuhan · China2021–2023 · 2 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 · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

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

 Works by year

2020s

20231 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency (2019 · 884 citations)
    The paper tackles adversarial attacks on neural text classifiers. Such attacks are harder to build than image attacks because words are discrete, and edits must stay lexically, grammatically and semantically sound. The authors propose PWWS (Probability Weighted Word Saliency), a greedy black-box attack built on…
  2. Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves (2023 · 62 citations)
    LLMs sometimes misread questions that look perfectly clear to people. The authors attribute this to a mismatch between how humans and models frame a query. For example, GPT-4 may interpret "even month" to mean any month whose number of days is even. To address this, they propose Rephrase and Respond (RaR), a…

All works

5 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Generating Natural Language Adversarial Examples through Probability Weighted Word SaliencyAnnual Meeting of the Association for Computational Linguistics (ACL)Shuhuai Ren, Yihe Deng, Kun He, et al.2019 · 884 citations
  2. Rephrase and Respond: Let Large Language Models Ask Better Questions for ThemselvesarXiv:2311.04205Yihe Deng, Weitong Zhang, Zixiang Chen, et al.2023 · 62 citations
  3. Towards Understanding the Mixture-Of-Experts Layer in Deep Learning ↗neural information processing systems · no summary yetZixiang Chen et al.2022 · 49 citations
  4. Towards Understanding Mixture of Experts in Deep Learning ↗arXiv (Cornell University) · no summary yetZixiang Chen et al.2022 · 37 citations
  5. Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP ↗arXiv (Cornell University) · no summary yetZixiang Chen et al.2023

 Co-authors here

3 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 328 times (of the 149,439 papers whose bibliographies we read); 39 citing papers with a page here →.

Updated

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