论文标题
意大利面：用于在叙述中建模参与者状态的数据集
PASTA: A Dataset for Modeling Participant States in Narratives
论文作者
论文摘要
通过其参与者的基本状态将叙述中的事件理解为一致的整体。通常，这些参与者状态不会被明确提及，而是由读者推断出来。理解叙述的模型也应同样推断这些隐式状态，甚至是关于这些变化对这些状态对叙事的影响的原因。为了促进这一目标，我们介绍了一个新的众包英语语言，参与者指出了面食。该数据集包含可推断的参与者状态；对每个状态的反事实扰动；如果反事实是真实的，那么故事的变化将是必要的。我们介绍了三个基于州的推理任务，以测试一个故事何时由故事启用，以修改以反事实状态为条件的故事，并解释鉴于经修订的故事，以解释最有可能的状态变化。实验表明，当今的LLM可以在某种程度上推理有关状态的建议，但是有很大的改进空间，尤其是在需要访问和具有不同知识类型的推理能力的问题（例如，物理，数值，事实）。
The events in a narrative are understood as a coherent whole via the underlying states of their participants. Often, these participant states are not explicitly mentioned, instead left to be inferred by the reader. A model that understands narratives should likewise infer these implicit states, and even reason about the impact of changes to these states on the narrative. To facilitate this goal, we introduce a new crowdsourced English-language, Participant States dataset, PASTA. This dataset contains inferable participant states; a counterfactual perturbation to each state; and the changes to the story that would be necessary if the counterfactual were true. We introduce three state-based reasoning tasks that test for the ability to infer when a state is entailed by a story, to revise a story conditioned on a counterfactual state, and to explain the most likely state change given a revised story. Experiments show that today's LLMs can reason about states to some degree, but there is large room for improvement, especially in problems requiring access and ability to reason with diverse types of knowledge (e.g. physical, numerical, factual).
