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Anna A. Ivanova

Anna A. Ivanova has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Carina Kauf, Alessandro Lenci, Emmanuele Chersoni.

2 papers in our library · 2023–2024

OpenAlex · ORCID · Wikidata · h-index 12 · 34 works · 867 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 54 times in our library · ⭐ Paper Stars ranking: #7108

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

🎓Georgia Institute of TechnologyAtlanta · United States2023–2026 · 27 paperscurrent🎓Massachusetts Institute of TechnologyCambridge · United States2017–2024 · 22 papersEducation since 2017Employment 2022–2023🔬McGovern Institute for Brain ResearchCambridge · United States2019–2024 · 11 papers🎓University of MiamiCoral Gables · United States2013–2017 · 1 paperEducation 2013–2017

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 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

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Better Way to Do Masked Language Model Scoring (2023 · 53 citations)
    Masked language models such as BERT have no natural way to assign a probability to a whole sentence. Researchers therefore commonly use the pseudo-log-likelihood (PLL) of Salazar et al. (2020), which masks each token in turn and adds up the log probabilities. Kauf and Ivanova show that this standard variant overrates…
  2. Log Probabilities Are a Reliable Estimate of Semantic Plausibility in Base and Instruction-Tuned Language Models (2024 · 29 citations)
    The paper asks whether explicit prompting or log probabilities (LogProbs) give a better measure of how much language models know about semantic plausibility, which serves as a proxy for world knowledge.

 Co-authors here

4 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 54 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

Updated

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