Analysing Lightweight Large Language Models for Biomedical Named Entity Recognition on Diverse Ouput Formats

ArXi:2604.25920v1 Announce Type: cross Despite their strong linguistic capabilities, Large Language Models (LLMs) are computationally demanding and require substantial resources for fine-tuning, which is unadapted to privacy and budget constraints of many healthcare settings. To address this, we present an experimental analysis focused on Biomedical Named Entity Recognition using lightweight LLMs, we evaluate the impact of different output formats on model performance.