arXiv:2606.31039v1 Announce Type: new 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
The proliferation and increasing sophistication of LLMs necessitate a deeper understanding of their vulnerabilities to manipulative inputs, particularly as they are integrated into critical systems.
A strategic reader should care because the robustness of AI systems against logical fallacies directly impacts their reliability, trustworthiness, and susceptibility to manipulation in areas like information warfare, policy-making, and critical decision support.
The introduction of a specialized benchmark like LoFa shifts the focus from merely identifying fallacies to directly evaluating and improving LLMs' resilience against attempts at fallacious persuasion, moving towards more robust and less manipulable AI.
- · AI safety researchers
- · Developers of robust LLMs
- · Sectors reliant on unbiased AI analysis
- · Malicious actors employing rhetoric
- · LLMs with poor logical reasoning
- · Systems relying on easily manipulated AI
Ongoing development of LLMs will increasingly incorporate mechanisms to improve robustness against logical fallacies.
Public trust in AI systems for critical functions will rise as their resistance to manipulative inputs improves.
The benchmark could become a standard for regulatory compliance, mandating minimum fallacy robustness for certain AI applications.
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Read at arXiv cs.CL