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Can large language models generate salient negative statements?
We examine the ability of large language models (LLMs) to generate salient (interesting) negative statements about real-world entities; an emerging research topic of the last few years.
Completeness, Recall, and Negation in Open-World Knowledge Bases: A Survey
General-purpose knowledge bases (KBs) are a cornerstone of knowledge-centric AI.
UnCommonSense: Informative Negative Knowledge about Everyday Concepts
This way, positive statements about comparable concepts that are absent for the target concept become seeds for negative statement candidates.
Enriching Knowledge Bases with Interesting Negative Statements
Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance, any effort towards compiling them needs a tight coupling with ranking.
Negative Statements Considered Useful
Negative statements are useful to overcome limitations of question answering systems that are mainly geared for positive questions; they can also contribute to informative summaries of entities.
