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. We probe the LLMs using zero- and k-shot
unconstrained probes, and compare with traditional methods for negation
generation, i.e., pattern-based textual extractions and knowledge-graph-based
inferences, as well as crowdsourced gold statements. We measure the correctness
and salience of the generated lists about subjects from different domains. Our
evaluation shows that guided probes do in fact improve the quality of generated
negatives, compared to the zero-shot variant. Nevertheless, using both prompts,
LLMs still struggle with the notion of factuality of negatives, frequently
generating many ambiguous statements, or statements with negative keywords but
a positive meaning.

本文通过使用零样本和有向样本的方式，比较了大型语言模型、基于文本提取和基于知识图谱的负向语句生成等不同方法在生成关于不同主题下实体的正确性和显著性方面的表现，结果显示有向样本有利于提高生成负向语句的质量，但大型语言模型在理解负向语句的真实性方面仍然存在困难。