Lightweight LLMs Show Promise for Biomedical Named Entity Recognition
A new study from arXiv explores the use of lightweight Large Language Models (LLMs) for Biomedical Named Entity Recognition, addressing computational demands and privacy constraints in healthcare. The research evaluates how different output formats affect model performance, finding that lightweight LLMs can achieve competitive results compared to larger models. Instruction tuning across many distinct formats does not improve performance, but certain formats consistently yield better outcomes. The study highlights the potential of lightweight LLMs as effective alternatives for biomedical information extraction.
Key facts
- Lightweight LLMs can achieve competitive performance for Biomedical Named Entity Recognition.
- Instruction tuning over many distinct formats does not improve performance.
- Certain output formats are consistently associated with better performance.
- Large Language Models are computationally demanding and require substantial resources for fine-tuning.
- Privacy and budget constraints in healthcare settings motivate the need for lightweight alternatives.
- The study is published on arXiv under Computer Science > Computation and Language.
- The submission history is available on arXiv.
- The paper is identified by arXiv ID 2604.25920.
Entities
Institutions
- arXiv