Theory-Grounded Evaluation of Human-Like Fallacy Patterns in LLM Reasoning

• URL: http://arxiv.org/abs/2506.11128v2
• Date: Fri, 24 Oct 2025 11:47:35 GMT
• Title: Theory-Grounded Evaluation of Human-Like Fallacy Patterns in LLM Reasoning
• Authors: Andrew Keenan Richardson, Ryan Othniel Kearns, Sean Moss, Vincent Wang-Mascianica, Philipp Koralus,
• Abstract summary: We study logical reasoning in language models by asking whether their errors follow established human fallacy.<n>For each response, we judge logical reasoning and correctness when it matches an ETRpredicted fallacy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study logical reasoning in language models by asking whether their errors follow established human fallacy patterns. Using the Erotetic Theory of Reasoning (ETR) and its open-source implementation, PyETR, we programmatically generate 383 formally specified reasoning problems and evaluate 38 models. For each response, we judge logical correctness and, when incorrect, whether it matches an ETR-predicted fallacy. Two results stand out: (i) as a capability proxy (Chatbot Arena Elo) increases, a larger share of a model's incorrect answers are ETR-predicted fallacies $(\rho=0.360, p=0.0265)$, while overall correctness on this dataset shows no correlation with capability; (ii) reversing premise order significantly reduces fallacy production for many models, mirroring human order effects. Methodologically, PyETR provides an open-source pipeline for unbounded, synthetic, contamination-resistant reasoning tests linked to a cognitive theory, enabling analyses that focus on error composition rather than error rate.

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