Strategic Role of Foundation Models at the Lamarr Institute
Foundation models are large-scale Artificial Intelligence models trained on massive and diverse datasets. Unlike traditional AI systems built for narrow tasks, foundation models learn general representations that can be adapted to a wide variety of downstream applications through fine-tuning. They span multiple modalities, including text, vision, speech, and structured data, and form the technological backbone of many generative AI systems. Large language models (LLMs) are currently the most prominent example of innovations made possible by this paradigm.
Foundation models are poised to fundamentally reshape economies as well as personal interactions with technology. They have become a strategic pillar for ensuring digital and economic sovereignty in Europe. Recognizing this transformative potential, researchers at the Lamarr Institute are committed to advancing high-quality, targeted research that deepens understanding and drives development of foundation models across key scientific and technological dimensions.
Research Approach
To advance foundation models, our research focuses on five interrelated areas: large-scale data curation and synthesis, curriculum learning, multimodal learning, reasoning, and knowledge distillation. These areas are unified by a central guiding principle: multilingualism. Rather than treating multilingualism as an isolated objective, we embed it across all research areas to ensure that foundation models deliver strong performance across languages and foster more inclusive access to AI technologies.
Focus on Data-centric Development
High-quality training data is a core ingredient for developing competitive and training-efficient foundation models. However, insights into data curation and composition are often kept proprietary by frontier AI labs, creating a critical gap in the research community. At the Lamarr Institute, we place strong emphasis on data-centric research, pursuing improvements in model performance and data efficiency through careful dataset composition, rigorous quality assurance, and advanced curation strategies.
