Logical Fallacy Detection
This addresses the problem of identifying fallacious arguments, particularly in misinformation contexts like climate change, but is incremental as it builds on existing methods with a new dataset and task.
The paper tackles the problem of detecting logical fallacies in text, which is challenging due to the need to understand argument structure, and shows that a simple structure-aware classifier outperforms the best language model by 5.46% on a general dataset and 4.51% on a climate change-specific dataset.
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