Entities and events have long been regarded as the crux of machine reasoning. Specifically, procedural texts have received increasing attention due to the dynamic nature of involved entities and events. Existing work has exclusively focused on entity state tracking (e.g., the temperature of a pan) or counterfactual event reasoning (e.g., how likely am I to burn myself by touching the pan), while these two tasks are tightly intertwined. In this work, we propose CREPE, the first benchmark on causal reasoning about event plausibility based on entity states. We experiment with strong large language models and show that most models including GPT3 perform close to chance of .30 F1, lagging far behind the human performance of .87 F1. Inspired by the finding that structured representations such as programming languages benefits event reasoning as a prompt to code language models such as Codex, we creatively inject the causal relations between entities and events through intermediate variables and boost the performance to .67 to .72 F1. Our proposed event representation not only allows for knowledge injection, but also marks the first successful attempt of chain-of-thought reasoning with code language models.

翻译：实体与事件长期以来被视为机器推理的核心。具体而言，过程文本因其涉及的实体和事件的动态特性而受到越来越多的关注。现有工作主要聚焦于实体状态追踪（例如锅的温度）或反事实事件推理（例如触摸锅时被烫伤的可能性），然而这两项任务紧密交织。在本研究中，我们提出CREPE，这是首个基于实体状态进行事件合理性的因果推理基准。我们使用强大的大型语言模型进行实验，结果显示包括GPT3在内的大多数模型性能接近随机水平（F1值约0.30），远低于人类表现的0.87 F1。受结构化表示（如编程语言）作为提示能增强代码语言模型（如Codex）事件推理这一发现的启发，我们创新性地通过中间变量注入实体与事件之间的因果关系，将性能提升至0.67至0.72 F1。我们提出的事件表示不仅支持知识注入，还标志着首次成功实现代码语言模型的思维链推理。