论文标题
事件领域：通过语言模型的实体状态事件推理
EvEntS ReaLM: Event Reasoning of Entity States via Language Models
论文作者
论文摘要
本文研究了事件含义的模型。具体而言，模型如何通过定位其对物理属性的理解来预测实体状态变化。名义上，大型语言模型（LLM）已接触到有关对象如何相互作用的程序知识，但我们的基准测试表明他们对世界没有推理。相反，我们还证明了现有方法通常通过不当任务编码歪曲了LLM的惊人能力，并且适当的模型提示可以显着提高多个任务中报告的基线结果的性能。特别是，我们的结果表明，我们的提示技术对于看不见的属性（室外）或仅当可用数据时特别有用。
This paper investigates models of event implications. Specifically, how well models predict entity state-changes, by targeting their understanding of physical attributes. Nominally, Large Language models (LLM) have been exposed to procedural knowledge about how objects interact, yet our benchmarking shows they fail to reason about the world. Conversely, we also demonstrate that existing approaches often misrepresent the surprising abilities of LLMs via improper task encodings and that proper model prompting can dramatically improve performance of reported baseline results across multiple tasks. In particular, our results indicate that our prompting technique is especially useful for unseen attributes (out-of-domain) or when only limited data is available.
