用大模型提取谬误模式，提升自动识别准确率
Beyond Logical Forms: LLM-Extracted Patterns for Fallacy Classification
- 通过大模型从谬误案例中归纳抽象推理模式
- 在零样本和少样本下均显著优于基线方法
- 适合需要高精度逻辑分析的文本审核场景
在信息传播速度极快的今天，逻辑谬误作为缺陷推理模式，加剧了信息失序。然而，谬误常以微妙形式出现，使自动化分类变得困难。本研究探讨将抽象逻辑结构与上下文语言线索结合是否有助于谬误分类，提出一种框架，利用大语言模型（LLMs）从谬误实例及其解释中归纳提取此类模式。我们在不同LLM及零样本、单样本设置下评估这些模式的影响，结果表明其在统计学上显著优于零样本基线，并超越现有方法。跨数据集实验验证了泛化能力，确立了数据驱动的模式提取是生成逻辑表示的有效方法。
原文摘要 · Abstract (English)
In today's fast-paced information era, logical fallacies, defined as defective patterns of reasoning, inevitably contribute to the growth of information disorder. However, often fallacies appear in nuanced forms that complicate automated classification. In this study, we investigate whether merging abstract logical structures with context-level linguistic cues proves beneficial for fallacy classification, developing a framework that inductively extracts such patterns from fallacious examples and their explanations using Large Language Models (LLMs). We evaluate the impact of these patterns across different LLMs and experimental zero- and one-shot configurations, showing statistically significant improvements over zero-shot baselines and outperforming competing approaches. Cross-dataset experiments validate generalization, establishing data-driven pattern extraction as an effective method for generating logical representations.
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