两篇新研究论文探讨了生物医学实体链接（BEL）和命名实体识别（NER）的挑战和潜在解决方案。一篇论文介绍了一个名为BeLink的系统，该系统使用指令微调的生成模型来提高BEL的效率和准确性，在链接准确性和推理时间方面均有显著提升。第二篇论文提出了一个诊断框架，以更好地理解现有的生物医学NER和EL基准测试实际衡量了什么，突出了影响评估信号和泛化需求的语料库属性的显著差异。 AI
影响 这些论文在生物医学自然语言处理领域取得了进展，有望提高医学研究和应用中工具的效率和可解释性。
排序理由 两篇在arXiv上发表的学术论文，讨论了生物医学自然语言处理任务的方法和分析。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2605.22501v1 Announce Type: new Abstract: Despite recent progress, Biomedical Entity Linking (BEL) with large language models (LLMs) remains computationally inefficient and challenging to deploy in practical settings. In this work, we demonstrate that instruction-tuning of …
Despite recent progress, Biomedical Entity Linking (BEL) with large language models (LLMs) remains computationally inefficient and challenging to deploy in practical settings. In this work, we demonstrate that instruction-tuning of open-source generative models can offer an effec…
Biomedical named entity recognition (NER) and entity linking (EL) strongly depend on annotated corpora, but the utility of these resources for benchmarking is often assumed rather than characterized. We present a corpus-centric framework for diagnosing benchmark-relevant properti…