发表机构
University of Edinburgh（爱丁堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对科学论断的语义模糊性，提出泛化分类体系NLPGenX及LLM框架NLPGenA，自动分类NLP论文句子为5类，构建大规模标注数据集NLPGens，分析泛化使用及其与引用等的关联。
AI 中文摘要
泛化（Generalisation）在科学交流中很常见，尽管它们在语义上是模糊的。需要一种自动化方法来根据其泛化程度识别和分类论断，以帮助检测对泛化的过度依赖以及对科学发现的可能误述。我们提出了科学领域中泛化的全面分类体系NLPGenX，该体系根据论断在文本中的泛化程度和框架对其进行标注。我们通过一个基于LLM的框架NLPGenA实现了这一分类体系，该框架自动将科学文章中的句子分类为5种不同的泛化类别。我们与人工标注者一起验证了我们的框架，并使用该框架构建了一个大规模的数据集，其中包含根据泛化性标注的NLP论文，并附有关于对冲（hedging）和模糊描述符（vague descriptors）的辅助标签（NLPGens）。我们使用NLPGens来分析NLP论文中跨多个会议和子领域的泛化使用情况，并检查其与引用次数、对冲和模糊描述符的关联。
英文摘要
Generalisations are common in scientific communication, even though they are semantically ambiguous. An automated method is needed to identify and categorise claims according to their level of generalisation, in order help detect an over-reliance on generalisations and possible misrepresentations of scientific findings. We introduce a comprehensive taxonomy of generalisations in the scientific domain, NLPGenX, which labels claims according to their level of generality and framing within the text. We operationalise this taxonomy with an LLM-powered framework, NLPGenA, that automatically classifies sentences from scientific articles into 5 different generalisation classes. We validate our framework with human annotators and use the framework to construct a large-scale dataset of NLP papers annotated according to generality, with auxiliary labels for hedging and vague descriptors (NLPGens). We use NLPGens to analyse the use of generalisations in NLP papers across multiple venues and subdomains, and to examine associations with citation counts, hedging, and vague descriptors.
CommentsEMNLP 2026 Main; the dataset and code are available at https://github.com/cx-diao/nlpgen
