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Seth Neel

Seth Neel has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining; often with Himabindu Lakkaraju, Martin Pawelczyk.

2 papers in our library · 2023–2026

OpenAlex · h-index 13 · 54 works · 1,282 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 92 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #8319

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

🎓Harvard UniversityCambridge · United States2014–2026 · 11 paperscurrent🎓University of PennsylvaniaPhiladelphia · United States2016–2025 · 20 papers🏢GoogleMountain View · United States2024–2025 · 2 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.

 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 worksComputer Science · 2 worksSocial 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

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. In-Context Unlearning: Language Models as Few Shot Unlearners (2023 · 81 citations)
    Privacy rules such as the Right to be Forgotten, along with copyright takedown demands, can require removing the effect of particular training examples from a deployed model.
  2. CheckMIABench: Firm Foundations For Membership Inference Attacks on Language Models (2026)
    Evaluating membership inference attacks (MIAs) on LLMs is hard because member and non-member sets often differ in distribution.

All works

4 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. In-Context Unlearning: Language Models as Few Shot UnlearnersarXiv:2310.07579Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju2023 · 81 citations
  2. Privacy Issues in Large Language Models: A Survey ↗arXiv:2312.06717 · no summary yetSeth Neel, Peter Chang2023 · 30 citations
  3. Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice ↗SSRN Electronic Journal · no summary yetA. Feder Cooper et al.2025 · 12 citations
  4. CheckMIABench: Firm Foundations For Membership Inference Attacks on Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jeffrey George Wang, Jason Wang, Marvin Li, et al.2026

 Co-authors here

2 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 92 times (of the 149,439 papers whose bibliographies we read); 23 citing papers with a page here →.

Updated

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