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Florian Hinterwimmer

Florian Hinterwimmer has 2 papers in our library, in 2026, most of them on Language models & pretraining; often with Daniel Rueckert, Marton Szep, Rüdiger von Eisenhart-Rothe.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 13 · 89 works · 554 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1 time in our library · ⭐ Paper Stars ranking: #10275

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

🎓Technical University of MunichMunich · Germany2021–2026 · 79 paperscurrent🏥TUM KlinikumMunich · Germany2021–2026 · 72 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 2 works

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide (2026 · 7 citations)
    This TACL survey examines how to adapt large language models when labeled data is scarce. That situation is common for low-resource languages, specialized fields such as medicine or law, and deployments with limited compute. The authors introduce no new dataset or model. Instead, they organize recent literature…
  2. Unintended Memorization of Sensitive Information in Fine-Tuned Language Models (2026)
    The paper examines a privacy risk that has received little attention. When an LLM is fine-tuned on sensitive records, it may memorize personal data (PII) that appears only in the inputs, even though the training targets never require it. The authors formalize this input-only memorization under a black-box threat…

 Co-authors here

3 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 1 time (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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