Elamur Research⌕
☰
home / authors / Fatemehsadat Mireshghallah

Fatemehsadat Mireshghallah

Fatemehsadat Mireshghallah has 2 papers in our library, in 2022–2023, most of them on Language models & pretraining; often with Taylor Berg-Kirkpatrick, Bernhard Schölkopf, Kartik Goyal.

2 papers in our library · 2022–2023

OpenAlex · ORCID · h-index 15 · 53 works · 996 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 134 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4153

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

🎓Carnegie Mellon UniversityPittsburgh · United States2021–2026 · 2 paperscurrent🎓University of California San DiegoSan Diego · United States2020–2023 · 24 papers🎓University of WashingtonSeattle · United States2021–2023 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workSocial Sciences · 1 workSociology and Political Science · 1 work

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

 Works by year

2020s

20231 paperMembership Inference Attacks against Language Models via Neighbourhood Comparison20221 paperQuantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks

 Papers

The most cited first (OpenAlex and our library).

  1. Membership Inference Attacks against Language Models via Neighbourhood Comparison (2023 · 162 citations)
    The paper examines membership inference attacks (MIAs) on language models, which try to determine whether a specific text was part of a model's training set.
  2. Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks (2022 · 145 citations)
    Earlier studies suggested that masked language models (MLMs) leak little of their training data. The authors argue this conclusion came from weak attacks that only threshold the target model's loss. Such a loss partly reflects how intrinsically hard a sample is, so it is a poor membership signal. They propose a…

All works

3 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. Membership Inference Attacks against Language Models via Neighbourhood ComparisonJustus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, et al.Findings of the Association for Computational Linguistics · 162 citations
  2. Quantifying Privacy Risks of Masked Language Models Using Membership Inference AttacksFatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, et al.Conference on Empirical Methods in Natural Language Processing (EMNLP) · 145 citations
  3. Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness · original ↗ · no summary yet · Proceedings of the 2021 Conference on Empirical Methods in Natural Language…Fatemehsadat Mireshghallah, Taylor Berg-Kirkpatrick2021 · 15 citations

 Co-authors here

6 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 134 times (of the 141,840 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/fatemehsadat-mireshghallah/