Importierter Kurs aus dem LSF
Foundation models are pretrained once on broad and heterogeneous data distributions; however, real-world deployment typically requires reliable performance in narrow, specialized domains. This seminar will discuss fundamental and recent approaches to address the following question: How do we specialize foundation models to a target domain under computational and data constraints?
To explore this question, we will read and discuss basic and state-of-the-art methods on how to systematically adapt and fine-tune large models through parameter-efficient fine-tuning, prompt engineering, test-time adaptation, context augmentation and many more.
See also information on my Website
Foundation models are pretrained once on broad and heterogeneous data distributions; however, real-world deployment typically requires reliable performance in narrow, specialized domains. This seminar will discuss fundamental and recent approaches to address the following question: How do we specialize foundation models to a target domain under computational and data constraints?
To explore this question, we will read and discuss basic and state-of-the-art methods on how to systematically adapt and fine-tune large models through parameter-efficient fine-tuning, prompt engineering, test-time adaptation, context augmentation and many more.
See also information on my Website
- Lehrende:r: Katharina Eggensperger