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Benjamin L. Edelman

Benjamin L. Edelman has 2 papers in our library, in 2024, most of them on Language models & pretraining, Bias, fairness & safety; often with Abulhair Saparov, Daniel Paleka, Ekdeep Singh Lubana.

2 papers in our library · 2024

OpenAlex · ORCID · h-index 25 · 67 works · 3,720 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 76 times in our library · ⭐ Paper Stars ranking: #8427

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

🏥National Jewish HealthDenver · United States2005–2026 · 21 paperscurrent🎓Harvard UniversityCambridge · United States2005–2024 · 27 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 9The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksEngineering · 1 workSafety Research · 1 workSafety, Risk, Reliability and Quality · 1 workSocial Sciences · 1 workArts and Humanities · 1 workComputer Vision and Pattern Recognition · 1 workLanguage and Linguistics · 1 work

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

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Foundational Challenges in Assuring Alignment and Safety of Large Language Models (2024 · 87 citations)
    This agenda-style paper by Anwar and a large multi-institution team asks what still prevents large language models from being reliably aligned and safe.
  2. Transcendence: Generative Models Can Outperform The Experts That Train Them (2024 · 12 citations)
    Models trained with cross-entropy learn to mimic the distribution of their training data. This suggests they cannot outperform the humans who produced that data. The authors examine this assumption and define "transcendence" as a generative model earning higher reward than every expert behind its training set.

 Co-authors here

10 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 76 times (of the 149,439 papers whose bibliographies we read); 25 citing papers with a page here →.

Updated

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