AGENTIC-SYSTEMSApril 30, 20261 min read12 views

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

A new study evaluates lightweight Large Language Models (LLMs) for Biomedical Named Entity Recognition, revealing they can match the performance of larger models while being more cost-effective and privacy-friendly.

Computer Science > Computation and Language

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

View PDF HTML (experimental)Abstract: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. The results reveal that lightweight LLMs can achieve competitive performance compared to the larger models, highlighting their potential as lightweight yet effective alternatives for biomedical information extraction. Our analysis shows that instruction tuning over many distinct formats does not improve performance, but identifies several format consistently associated with better performance.

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