Simon Ostermann

Simon Ostermann serves as a Senior Lecturer at Saarland University and Senior Researcher & Deputy Director at the Multilinguality and Language Technology (MLT) lab of the German Research Center for Artificial Intelligence (DFKI). He leads the Efficient and Explainable NLP (E&E) research group and contributes to major projects including lorAI (Low Resource AI), TRAILS (Trustworthy Machines), PERKS (Procedural Knowledge), DAM-S (Semantic Search), and DisAI (Disinformation Combat).

His research centers on democratizing language technology through transparent, robust models—specializing in mechanistic interpretability to reverse-engineer LLM internals and enhance efficiency for low-resource languages. Key focus areas include reducing model size for constrained environments, improving cross-lingual transfer via adapters, and developing structured input techniques. His work bridges theoretical interpretability with practical applications in resource-limited settings.

2025 publications reveal concentrated efforts in low-resource adaptation (language adapters, graph-enhanced embeddings), explainable AI (counterfactual generation, conversational XAI datasets), and multilingual fact-checking systems. Notable trends include systematic neuron manipulation frameworks, rigorous evaluation of synthetic data strategies, and cross-lingual claim verification benchmarks.

Ostermann advises six PhD candidates (Anikina, Oguz, Bäumel, al Ghussin, Gurgurov, Vykopal) and multiple MSc students on topics spanning RAG hallucinations, multilabel classification, and adapter interpretability. His research receives funding through DFKI-led consortia with European and international partners focusing on trustworthy, efficient AI deployment.

The E&E group under his leadership drives innovation in efficient NLP through biweekly seminars, collaborative coding sessions, and partnerships with institutions like KInIT. Current initiatives prioritize green computing for language models and real-world deployment in industrial procedural knowledge systems.