Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding
This addresses a specific challenge in natural language processing for AI systems, but it is incremental as it builds on existing commonsense knowledge resources.
The paper tackled the problem that Large Language Models struggle with negation in natural language understanding by automatically augmenting existing commonsense knowledge corpora with negation, resulting in two new corpora with over 2M triples and showing that pre-training on these corpora improves negation understanding.
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.