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Katharina Reinecke

Katharina Reinecke has 3 papers in our library, in 2023–2025, most of them on Language models & pretraining; often with Maarten Sap, Jennifer Pan, Jillian Fisher.

3 papers in our library · 2023–2025

OpenAlex · ORCID · Wikidata · h-index 29 · 125 works · 3,706 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 105 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6060

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 WashingtonSeattle · United States2016–2026 · 86 paperscurrent🎓Carnegie Mellon UniversityPittsburgh · United States2023–2026 · 3 paperscurrent🎓University of MichiganAnn Arbor · United States2014–2024 · 9 papers🎓University of ZurichZurich · Switzerland2007–2011 · 9 papers🎓Harvard UniversityCambridge · United States2011–2013 · 5 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 8The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksSocial Sciences · 2 worksSafety Research · 2 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksGeneral Social 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

20252 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. NLPositionality: Characterizing Design Biases of Datasets and Models (2023 · 95 citations)
    NLP datasets and models often work better for some populations than others. These design biases frequently trace back to the positionality of their creators, meaning the identity, background and lived experience that shape their choices. Such choices are rarely documented, and many systems are only reachable through…
  2. NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models (2025 · 46 citations)
    Most cultural evaluations of LLMs ask models directly about norms, which shows what they know but not whether they can use that knowledge in a concrete user situation.
  3. Biased LLMs can Influence Political Decision-Making (2025 · 20 citations)
    The paper asks whether partisan bias in chat-based LLMs can change people's political views and choices during short, free-form conversations.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. NLPositionality: Characterizing Design Biases of Datasets and ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Sebastin Santy, Jenny Liang, Ronan Le Bras, et al.2023 · 95 citations
  2. NormAd: A Framework for Measuring the Cultural Adaptability of Large Language ModelsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Abhinav Rao, Akhila Yerukola, Vishwa Shah, et al.2025 · 46 citations
  3. Biased LLMs can Influence Political Decision-MakingAnnual Meeting of the Association for Computational Linguistics (ACL)Jillian Fisher, Shangbin Feng, Robert Aron, et al.2025 · 20 citations
  4. Visible to the Court: How AI Is (and Isn't) Litigated in U.S. Federal Court Opinions ↗arXiv:2607.23888 · no summary yetJulie Yu et al.2026

 Co-authors here

9 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 105 times (of the 149,439 papers whose bibliographies we read); 24 citing papers with a page here →.

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