Logical Fallacy Detection

• URL: http://arxiv.org/abs/2202.13758v3
• Date: Mon, 12 Dec 2022 04:47:49 GMT
• Title: Logical Fallacy Detection
• Authors: Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, Bernhard Sch\"olkopf
• Abstract summary: We propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text. We show that a simple structure-aware classifier outperforms the best language model by 5.46% on Logic and 4.51% on LogicClimate.
• Score: 40.06349885733248
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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

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