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Frederic Sala

Frederic Sala has 2 papers in our library, in 2020–2022, most of them on Language models & pretraining, Knowledge graphs & ontologies; often with Christopher Ré, Ines Chami, Avanika Narayan.

2 papers in our library · 2020–2022

OpenAlex · ORCID · h-index 29 · 134 works · 3,552 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 178 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #3897

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 Wisconsin–MadisonMadison · United States2021–2026 · 40 papersEmployment · Assistant Professor, Computer Sciencescurrent🎓Stanford UniversityStanford · United States2017–2026 · 13 paperscurrent🎓University of California, Los AngelesLos Angeles · United States2012–2018 · 31 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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 worksInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Knowledge graphs & ontologies · 1 workLanguage models & pretraining · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Knowledge graph · 1 work

 Works by year

2020s

20221 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Low-Dimensional Hyperbolic Knowledge Graph Embeddings (2020 · 429 citations)
    Knowledge graphs (KGs) are typically incomplete, so missing links are often predicted with entity and relation embeddings.
  2. Ask Me Anything: A simple strategy for prompting language models (2022 · 154 citations)
    Prompting large language models is fragile: minor wording changes can shift accuracy a lot, so practitioners often spend significant effort hand-tuning a single prompt per task.

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. Low-Dimensional Hyperbolic Knowledge Graph EmbeddingsAnnual Meeting of the Association for Computational Linguistics (ACL)Ines Chami, Adva Wolf, Da-Cheng Juan, et al.2020 · 429 citations
  2. Training Complex Models with Multi-Task Weak Supervision ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetAlexander Ratner et al.2019 · 175 citations
  3. Ask Me Anything: A simple strategy for prompting language modelsarXiv:2210.02441Simran Arora, Avanika Narayan, Mayee F. Chen, et al.2022 · 154 citations
  4. Generative Modeling Helps Weak Supervision (and Vice Versa) ↗arXiv (Cornell University) · no summary yetBenedikt Boecking et al.2022 · 1 citation
  5. Reference-Specific Unlearning Metrics Can Hide the Truth: A Reality Check ↗arXiv (Cornell University) · no summary yetSungjun Cho et al.2025

 Co-authors here

7 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 178 times (of the 149,439 papers whose bibliographies we read); 28 citing papers with a page here →.

Updated

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