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要素。具体而言，过程文本因所涉及实体与事件的动态特性而日益受到关注。现有研究仅聚焦于实体状态追踪（例如平底锅的温度）或反事实事件推理（例如触摸平底锅时烫伤自身的可能性），而这两项任务实则紧密交织。本文提出CREPE，首个基于实体状态进行事件合理性因果推理的基准数据集。我们采用强大规模语言模型进行实验，结果表明包括GPT3在内的多数模型性能接近随机水平（F1值0.30），显著落后于人类表现（F1值0.87）。受结构化表征（如编程语言）能提升代码语言模型（如Codex）事件推理能力的启发，我们创新性地通过中间变量注入实体与事件间的因果关系，将性能提升至0.67至0.72 F1值。所提出的事件表征不仅支持知识注入，更标志着代码语言模型实现思维链推理的首次成功尝试。