Grant Success: Our new DFG Research Unit "Bedeutungswandel in ressourcenarmen Domänen" has been officially approved! We are excited to develop new computational methods for the humanities.
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Grant Success: Our new DFG Research Unit "Bedeutungswandel in ressourcenarmen Domänen" has been officially approved! We are excited to develop new computational methods for the humanities.
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Soofi S is the first model of the Soofi project and marks the start of its first release phase. It is designed for industrial AI applications and developed on European infrastructure. The focus is on transparent, adaptable and sovereign AI deployment.
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Das Bayrische Staatsministerium für Wissenschaft und Kunst fördert ein neues Leuchtturmprojekt zur Stärkung der technologischen Souveränität Bayerns im Bereich Künstliche Intelligenz - mit dabei der LSX.
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How do you build scalable, transparent language models for the German language entirely from scratch? We had the opportunity to discuss exactly that at the DLR in Ulm. The focus was on our model families LLäMmlein (120M–7B) and ModernGBERT (138M–1B), as well as the unique challenges of purely German tokenization.
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From Brisbane with Insights: How "tame" is our German LLM family really? Our chair returns from a six-week research sabbatical at The University of Queensland with new findings on AI safety and "LLäMmlein".
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The paper by Luzia Keupp, Andreas Hotho, Stefan Dech and Heiko Paeth studies climate change and its impact on regional agriculture .
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We hosted this year’s BigData@Geo2.0 workshop with our project partners at the CAIDAS Building.
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We presented a keynote on our LLäMmlein and ModernGBERT models at the German National Library in Frankfurt, as part of the "Netzwerk maschinelle Verfahren in der Erschließung" symposium.
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We, the Data Science Chair and the Chair of Computer Philology and Modern German Literary History at CAIDAS, are part of the new large-scale project „Sovereign Open Source Foundational Models for European Intelligence“ (SOOFI)
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We participated in all GermEval tasks and placed first in two of the three Harmful Content Detection tasks!
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