English(EN) Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understanding

新数据集NegCue提升LLM否定理解能力，尤其在词缀提示词方面

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

一个名为NegCue的新数据集，包含超过180万个否定提示词样本，已被开发出来以应对语言模型中否定理解的挑战。该数据集包括单词、多词和词缀否定类型。在NegCue上进行进一步预训练表明，词缀否定能为LM和LLM带来最显著的否定理解能力提升，而传统的单次否定影响则较为温和。 AI

影响增强了LLM理解细微语言的能力，可能改进需要精确理解否定的应用。

排序理由该集群包含一篇研究论文，详细介绍了新的数据集和关于语言模型否定理解的发现。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Tian Tan, Eduardo Blanco · 2026-09-15 04:00

并非所有否定线索都相同：词缀否定能带来更好的否定理解

arXiv:2609.13685v1 Announce Type: cross Abstract: Negation remains a longstanding challenge for both language models (LMs) and large language models (LLMs). Prior work mainly focuses on a small set of high-frequency single-word negation cues, such as not and never, with limited e…