一个数据集用于评估语言模型中生物医学概念的上下文表示
Hossein Rouhizadeh1, Irina Nikishina2, Anthony Yazdani3
1Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland. hossein.rouhizadeh@unige.ch.
Scientific data
|May 4, 2024
概括
研究人员开发了一个新的基准数据集BioWiC,以评估语言模型在特定环境中如何理解生物医学术语. 这个工具有助于改善复杂的生物医学领域的自然语言处理.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 捕捉生物医学术语的上下文依赖的语义表示是一个重大挑战.
- 现有的评估基准不足以评估语言模型对生物医学概念的理解.
研究的目的:
- 引入BioWiC,这是一个新的数据集,旨在评估语言模型在特定背景下编码生物医学术语的能力.
- 建立一个基准来评估生物医疗机构中依赖上下文的嵌入.
主要方法:
- 开发BioWiC数据集,包括20156个实例和超过7400个独特的生物医学术语.
- 对BioWiC数据集的内在和外在评估.
- 使用各种歧视性和生成性大语言模型来建立基线性能的实验.
主要成果:
- 生物WiC是生物医学领域最大的 Word-in-Context (WiC) 数据集.
- 该数据集适合作为一个可靠的基准来评估生物医学文本中的上下文依赖嵌入.
- 在BioWiC基准上为大型语言模型建立了强大的基线性能.
结论:
- 在生物医学领域,BioWiC为推进自然语言理解提供了至关重要的资源.
- 该数据集有助于开发和评估用于生物医学应用的更复杂的语言模型.
- 未来的研究可以建立在改善生物医学NLP性能的既定基线上.
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