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Jan Brauner

Jan Brauner has 2 papers in our library, in 2022–2023, most of them on Evaluation & benchmarks; often with Matthias Samwald, Owain Evans, Simon Ott.

2 papers in our library · 2022–2023

OpenAlex · ORCID · Wikidata · h-index 19 · 41 works · 3,051 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 468 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #6514

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

🏛️European CommissionBrussels · Belgium2026 · 1 papercurrent🎓University of OxfordOxford · United Kingdom2020–2025 · 25 papers🎓Friedrich-Alexander-Universität Erlangen-NürnbergErlangen · Germany2013–2017 · 8 papers🎓Witten/Herdecke UniversityWitten · Germany2021–2023 · 4 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.

 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 worksDecision Sciences · 1 workComputer Vision and Pattern Recognition · 1 workStatistics, Probability and Uncertainty · 1 workPsychology · 1 workSocial Psychology · 1 workSocial Sciences · 1 workSociology and Political Science · 1 work

🧭 Topics 1Research subjects and languages the work is about.Evaluation & benchmarks · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Artificial intelligence · 1 work

 Works by year

2020s

20231 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Mapping global dynamics of benchmark creation and saturation in artificial intelligence (2022 · 93 citations)
    Benchmarks not only measure progress in AI but also set its direction. At the same time, many of them quickly reach saturation or are barely used. The authors proposed a way to concisely map the emergence, use, and saturation of benchmarks across entire fields. To do this, they took a Papers With Code dump and built…
  2. How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions (2023 · 30 citations)
    This work addresses detecting when a large language model gives a false answer even though it demonstrably knows the correct one.

All works

5 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. Managing extreme AI risks amid rapid progress ↗Science · no summary yetYoshua Bengio et al.2024 · 382 citations
  2. Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training ↗arXiv:2401.05566 · no summary yetEvan Hubinger et al.2024 · 123 citations
  3. Mapping global dynamics of benchmark creation and saturation in artificial intelligenceNature CommunicationsSimon Ott, Adriano Barbosa-Silva, Kathrin Blagec, et al.2022 · 93 citations
  4. Question Decomposition Improves the Faithfulness of Model-Generated Reasoning ↗arXiv (Cornell University) · no summary yetAnsh Radhakrishnan et al.2023 · 74 citations
  5. How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated QuestionsarXiv:2309.15840Lorenzo Pacchiardi, Alex J. Chan, Soren Mindermann, et al.2023 · 30 citations

 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 468 times (of the 149,439 papers whose bibliographies we read); 86 citing papers with a page here →.

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