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Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding

arXiv 2604.19921 ↗First posted 21 Apr 2026cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 33 references that are in the map. First posted on arXiv 21 Apr 2026.

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References · 26

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14 more
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