German Medical NER with BERT and LLMs:
The Impact of Training Data Size  

Named Entity Recognition (NER) in the medical domain often presents significant challenges due to the complexity and specificity of medical terminology, especially in lower-resource settings where annotated data is scarce. We have explored the performance of an exemplary large language model and a BERT-based model in the context of NER for German medical texts.  

• How did we methodologically compare LLaMA3.1 and GBERT?

• What is th impact of different data sizes for training and their performance to simulate lower-resource conditions?

• How did both LLMs compare in our evaluation