Truth or Sophistry? LoFa: A Benchmark for LLM Robustness Against Logical Fallacies

• URL: http://arxiv.org/abs/2606.31039v1
• Date: Tue, 30 Jun 2026 02:17:45 GMT
• Title: Truth or Sophistry? LoFa: A Benchmark for LLM Robustness Against Logical Fallacies
• Abstract summary: Large Language Models (LLMs) exhibit strong semantic capabilities, yet their resilience to manipulative linguistic patterns such as logical fallacies remains underexplored.<n>We introduce LoFa (Logical Fallacy), a benchmark for evaluating LLM robustness against fallacies.<n>We also propose Logical Fallacy Resistance at k (LFR@k), a metric that quantifies resistance to fallacious attacks.
• Score: 51.39569894108518
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) exhibit strong semantic capabilities, yet their resilience to manipulative linguistic patterns such as logical fallacies remains underexplored. Prior work has primarily examined whether LLMs can identify or classify fallacies, leaving their robustness against fallacious persuasion insufficiently studied. To address this gap, we introduce LoFa (Logical Fallacy), a comprehensive benchmark for evaluating LLM robustness against fallacies. LoFa is constructed through a multi-agent pipeline that pairs factual questions with fallacious arguments, and is accompanied by a multi-round debate framework for assessing model resilience under sustained adversarial persuasion. To disentangle fallacy robustness from a model's inherent knowledge limitations, we further propose Logical Fallacy Resistance at k (LFR@k), a metric that quantifies resistance to fallacious attacks. Experiments show that LLMs exhibit varying levels of robustness across different fallacy types, revealing distinct vulnerability profiles among models.

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