Abstract
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.
Abstract (translated)
实体和事件一直被视为机器推理的核心。特别是,程序流程文本因其涉及实体和事件的动态性质而日益受到关注。现有的工作主要关注实体状态追踪(例如平底锅的温度)或反事实事件推理(例如,我触摸平底锅是否可能会导致自己受伤),而这两个任务密切相关。在本文中,我们提出了CREPE,即基于实体状态的因果推理基准。我们与强大的大型语言模型进行了实验,表明,大多数模型,包括GPT3,的表现接近于0.30 F1的概率,远远落后于人类表现0.87 F1。受到发现结构化表示,如编程语言,对事件推理的好处,即作为代码语言模型的提示,启发,我们通过中间变量创造性地注入实体和事件之间的因果关系,并将性能提高至0.67到0.72 F1。我们提出的事件表示不仅允许知识注入,还标志着第一个成功尝试使用代码语言模型的思考序列推理。
URL
https://arxiv.org/abs/2301.10896