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Sebastian Farquhar

Sebastian Farquhar has 2 papers in our library, in 2023, most of them on Text generation, Evaluation & benchmarks; often with Lorenz Kuhn, Yarin Gal.

2 papers in our library · 2023

OpenAlex · ORCID · h-index 14 · 48 works · 2,377 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 506 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #4090

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

🎓University of OxfordOxford · United Kingdom2017–2026 · 21 paperscurrent🤝Science OxfordOxford · United Kingdom2026 · 1 papercurrent🏢Google DeepMindLondon · United Kingdom2022–2024 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 8The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workEngineering · 1 workSafety Research · 1 workSafety, Risk, Reliability and Quality · 1 workSocial Sciences · 1 work

🧭 Topics 2Research subjects and languages the work is about.Text generation · 1 workEvaluation & benchmarks · 1 work

 Works by year

2020s

20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation (2023 · 433 citations)
    The paper addresses how far the answers of large language models can be trusted in free-form text generation, particularly in question-answering systems.
  2. Model evaluation for extreme risks (2023 · 104 citations)
    This position paper, from authors at Google DeepMind, OpenAI, Anthropic, GovAI and academic institutions, argues that general-purpose AI models may develop harmful abilities their creators never targeted.

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. The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation ↗arXiv (Cornell University) · no summary yetMiles Brundage et al.2018 · 866 citations
  2. Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language GenerationarXiv:2302.09664Lorenz Kuhn, Yarin Gal, Sebastian Farquhar2023 · 433 citations
  3. Towards Robust Evaluations of Continual Learning ↗arXiv:1805.09733 · no summary yetSebastian Farquhar, Yarin Gal2018 · 185 citations
  4. Model evaluation for extreme risksarXiv:2305.15324Toby Shevlane, Sebastian Farquhar, Ben Garfinkel, et al.2023 · 104 citations
  5. Evaluating Frontier Models for Dangerous Capabilities ↗arXiv:2403.13793 · no summary yetMary Phuong et al.2024 · 36 citations

 Co-authors here

2 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 506 times (of the 149,439 papers whose bibliographies we read); 119 citing papers with a page here →.

Updated

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