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Christian Reuter

Christian Reuter has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Sentiment & emotion, Bias, fairness & safety; often with Markus Bayer.

2 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 37 · 463 works · 5,739 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 10 times in our library · ⭐ Paper Stars ranking: #9078

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

🎓Technische Universität DarmstadtDarmstadt · Germany2012–2026 · 187 paperscurrent🔬National Research Center for Applied Cybersecurity ATHENEDarmstadt · Germany2026 · 1 papercurrent🎓University of SiegenSiegen · Germany2008–2023 · 70 papers🔬Institut für Mikroelektronik StuttgartStuttgart · Germany1997–2017 · 11 papers🎓University of BonnBonn · Germany1999–2022 · 3 papers🎓Universität der Bundeswehr MünchenMunich · Germany2020–2021 · 2 papers🏢DaimlerStuttgart · Germany2016–2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 10 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 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 1 workBias, fairness & safety · 1 workEnglish · 1 workSentiment & emotion · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. ActiveLLM: Large Language Model-Based Active Learning for Textual Few-Shot Scenarios (2026 · 16 citations)
    Active learning (AL) tries to reduce annotation cost by choosing the examples that are most useful to label.
  2. A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in English (2025 · 4 citations)
    Most work on automatic hate speech detection treats it as a yes/no decision. Real users need more detail. Content moderators, law enforcement and reporting centres want to know the target, severity or legal relevance of a message, and one message can fit several of these categories at once. This survey follows the…

 Co-authors here

1 co-author 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 10 times (of the 149,439 papers whose bibliographies we read); 4 citing papers with a page here →.

Updated

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