Logical Fallacies
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Benchmarks
BIG-bench
Most implemented
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Logical Fallacy Detection
Socrates or Smartypants: Testing Logic Reasoning Capabilities of Large Language Models with Logic Programming-based Test Oracles
Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-Aware Prompt Formulation
RuozhiBench: Evaluating LLMs with Logical Fallacies and Misleading Premises
Papers
Truth or Sophistry? LoFa: A Benchmark for LLM Robustness Against Logical Fallacies
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 …
Logical FallaciesBeyond Logical Forms: LLM-Extracted Patterns for Fallacy Classification
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 com…
Logical FallaciesTackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation
Identifying logical fallacies in everyday discourse is challenging for many people. This challenge is amplified in the era of Large Language Models (LLMs), where malicious agents can deploy fallacious arguments to dissem…
Logical FallaciesLabel Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs
As AI-generated and AI-assisted content floods online spaces, source labels attached to such content can distort human reasoning judgments, with downstream consequences for moderation, evaluation, and decision-making. Wh…
Logical FallaciesMath Education Digital Shadows for facilitating learning with LLMs: Math performance, anxiety and confidence in simulated students and AIs
To enhance LLMs' impact on math education, we need data on their mathematical prowess and biases across prompts. To fill this gap, we introduce MEDS (Math Education Digital Shadows) as a dataset mapping how large languag…
Logical FallaciesEvian: Towards Explainable Visual Instruction-tuning Data Auditing
The efficacy of Large Vision-Language Models (LVLMs) is critically dependent on the quality of their training data, requiring a precise balance between visual fidelity and instruction-following capability. Existing datas…
Logical Fallacies