轻量级大模型在生物医学命名实体识别中表现优异，适配医疗场景资源限制。
Analysing Lightweight Large Language Models for Biomedical Named Entity Recognition on Diverse Ouput Formats
- 使用轻量级大模型进行生物医学命名实体识别，降低计算开销。
- 不同输出格式影响模型性能，部分格式显著提升识别效果。
- 适合预算有限、注重隐私保护的医疗AI应用开发人员参考。
尽管大型语言模型（LLMs）具备强大的语言能力，但其计算开销大，微调需要大量资源，难以适应许多医疗场景下的隐私与预算限制。为此，我们开展实验分析，研究轻量级大模型在生物医学命名实体识别中的表现，并评估不同输出格式对模型性能的影响。结果表明，轻量级模型可达到与大型模型相当的性能，展现出作为轻量高效替代方案的巨大潜力。分析还发现，对多种输出格式进行指令微调并未提升性能，但识别出若干在多个实验中持续带来更好表现的格式。
原文摘要 · Abstract (English)
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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