构建含否定的常识知识库，提升大模型对否定语义的理解能力。
Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding
- 自动为常识知识库添加否定三元组，生成200万条带条件关系的数据。
- 在新数据上预训练可显著提升模型对否定句的理解性能。
- 适合研究常识推理与否定理解的NLP研究人员使用。
否定是自然语言中常见且重要的语义特征，但大语言模型在涉及否定的任务中表现不佳。尽管常识知识已得到广泛研究，却缺乏对否定情境的探索。本文表明，包含否定的常识知识对模型理解构成挑战。我们提出一种新方法，自动为现有常识知识语料库添加否定内容，生成两个包含超过200万条三元组、具有若-则关系的新语料库。此外，在这些语料库上对大语言模型进行预训练，能有效提升其对否定的理解能力。
原文摘要 · Abstract (English)
Negation is a common and important semantic feature in natural language, yet Large Language Models (LLMs) struggle when negation is involved in natural language understanding tasks. Commonsense knowledge, on the other hand, despite being a well-studied topic, lacks investigations involving negation. In this work, we show that commonsense knowledge with negation is challenging for models to understand. We present a novel approach to automatically augment existing commonsense knowledge corpora with negation, yielding two new corpora containing over 2M triples with if-then relations. In addition, pre-training LLMs on our corpora benefits negation understanding.
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