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Beliz Gunel

Beliz Gunel has 2 papers in our library, in 2020–2021, most of them on Language models & pretraining; often with Alexis Conneau, Jingfei Du, Edouard Grave.

2 papers in our library · 2020–2021

OpenAlex · ORCID · h-index 10 · 33 works · 772 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 224 times in our library · ⭐ Paper Stars ranking: #3815

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

🎓Stanford UniversityStanford · United States2020–2025 · 10 papers🏢GoogleMountain View · United States2023–2025 · 6 papers🎓University of California, BerkeleyBerkeley · United States2015–2018 · 4 papers🏢Google DeepMindLondon · United Kingdom2024–2025 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20211 paperSelf-training Improves Pre-training for Natural Language Understanding20201 paperSupervised Contrastive Learning for Pre-trained Language Model Fine-tuning

 Papers

The most cited first (OpenAlex and our library).

  1. Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning (2020 · 216 citations)
    Fine-tuning a pre-trained language model with cross-entropy alone tends to generalize poorly when labels are scarce, reacts badly to noisy labels, and produces results that vary a lot between runs.
  2. Self-training Improves Pre-training for Natural Language Understanding (2021 · 169 citations)
    Self-training uses a teacher model to label unannotated examples for a student model. It usually assumes that unlabeled text from the target task's domain is already available, and that assumption breaks down for many low-resource tasks. The authors ask whether self-training still helps on top of strong…

 Co-authors here

8 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 224 times (of the 141,840 papers whose bibliographies we read); 14 citing papers with a page here →.

Updated

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