大型语言模型能否生成显著的否定陈述？
Can large language models generate salient negative statements?
AI总结：
本文研究大型语言模型生成显著否定陈述的能力，通过零样本和k样本探针与传统方法比较，发现有引导探针能提升质量，但LLMs在否定事实性方面仍存在不足。
摘要：
我们考察了大型语言模型（LLMs）生成关于现实世界实体的显著（有趣的）否定陈述的能力；这是近几年来一个新兴的研究课题。我们使用零样本和k样本无约束探针来探测LLMs，并与传统的否定生成方法进行比较，即基于模式的文本抽取和基于知识图谱的推理，以及众包的金标准陈述。我们测量了来自不同领域的主体的生成列表的正确性和显著性。我们的评估表明，与零样本变体相比，有引导的探针确实提高了生成否定陈述的质量。然而，使用两种提示时，LLMs仍然难以把握否定的事实性概念，经常生成许多模糊的陈述，或带有否定关键词但具有肯定含义的陈述。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
