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Niloofar Mireshghallah

Niloofar Mireshghallah has 3 papers in our library, in 2024, most of them on Language models & pretraining, Bias, fairness & safety; often with Yejin Choi, Liwei Jiang, Nouha Dziri.

3 papers in our library · 2024

OpenAlex · h-index 6 · 67 works · 117 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 266 times in our library · ⭐ Paper Stars ranking: #4166

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 States2024–2026 · 19 paperscurrent🎓University of WashingtonSeattle · United States2023–2025 · 24 papers🎓University of California San DiegoSan Diego · United States2023–2025 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksBias, fairness & safety · 1 work

 Works by year

2020s

20243 papersNiloofar Mireshghallah's papers of 2024

 Papers

The most cited first (OpenAlex and our library).

  1. A Roadmap to Pluralistic Alignment (2024 · 135 citations)
    This ICML 2024 position paper argues that aligning AI to an "average" human preference ignores real disagreement among people. Using LLMs as a testbed, the authors define three ways a model can be pluralistic:
  2. WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models (2024 · 89 citations)
    Frontier LLMs can still be jailbroken, and open safety-training data is scarce, since even open-weight models rarely publish theirs.
  3. Do Membership Inference Attacks Work on Large Language Models? (2024 · 80 citations)
    This paper asks whether standard membership inference attacks (MIAs) can tell if a given text was in an LLM's pre-training data.

 Co-authors here

12 of 20 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 266 times (of the 141,840 papers whose bibliographies we read); 12 citing papers with a page here →.

Updated

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