论文标题
逻辑谬误检测
Logical Fallacy Detection
论文作者
论文摘要
推理是人类智力的核心。但是，谬论的论点很普遍，并且一些加剧的问题，例如传播有关气候变化的错误信息。在本文中，我们提出了逻辑谬误检测的任务，并提供了在文本中通常发现的逻辑谬误的新数据集（逻辑），以及用于检测气候变化声明中逻辑谬误（逻辑climate）中逻辑谬误的附加挑战。检测逻辑谬误是一个困难的问题，因为该模型必须了解该参数的基本逻辑结构。我们发现，现有的经过预定的大语言模型在这项任务上表现不佳。相比之下，我们表明，简单的结构感知分类器在逻辑上的表现优于最佳语言模型，而逻辑cligitimate则优于4.51％。我们鼓励将来的工作探索这项任务，因为（a）它可以作为语言模型的新推理挑战，并且（b）它可以在解决错误信息传播方面具有潜在的应用。我们的数据集和代码可从https://github.com/causalnlp/logical-fallacy获得
Reasoning is central to human intelligence. However, fallacious arguments are common, and some exacerbate problems such as spreading misinformation about climate change. In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate). Detecting logical fallacies is a hard problem as the model must understand the underlying logical structure of the argument. We find that existing pretrained large language models perform poorly on this task. In contrast, we show that a simple structure-aware classifier outperforms the best language model by 5.46% on Logic and 4.51% on LogicClimate. We encourage future work to explore this task as (a) it can serve as a new reasoning challenge for language models, and (b) it can have potential applications in tackling the spread of misinformation. Our dataset and code are available at https://github.com/causalNLP/logical-fallacy
