Challenge: Existing computational approaches focus on logical structures of fallacies and argumentation schemes, ignoring the emotional dimension of argumentation.
Approach: They propose to use large language models to systematically change emotional appeals in fallacious arguments by using a computational approach.
Outcome: The proposed method reduces fallacy detection by 14.5% on average on human arguments with enjoyment over fear or sadness.

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Challenge: Emotions have been shown to play a role in argument convincingness, yet this aspect is underexplored in the natural language processing community.
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Beyond Detection: Evaluating Fallacy Awareness of LLMs in Interactive Scenarios (2026.acl-long)

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Challenge: Large Language Models fail to recognize fallacious reasoning in real-world interactions despite strong performance on static fallacy detection tasks.
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Missci: Reconstructing Fallacies in Misrepresented Science (2024.acl-long)

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Challenge: False or misleading narratives spread rapidly on social networks, posing challenges for non-experts in discerning credible information.
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Argument-based Detection and Classification of Fallacies in Political Debates (2023.emnlp-main)

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Challenge: Fallacies are arguments that employ faulty reasoning, causing inaccurate conclusions and invalid inferences . ad hominem fallacy is one of the most common fallacy labels used in political debates despite its use in many scenarios .
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A Logical Fallacy-Informed Framework for Argument Generation (2025.naacl-long)

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Challenge: Argument generation is crucial in daily life and has numerous online and offline applications.
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Challenge: Prior work on quality assessment has focused on numerical scoring and fallacy type-labeling tasks, without aiming to analyze fallacy logic structures.
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Logical Fallacy Detection (2022.findings-emnlp)

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Challenge: Existing language models perform poorly on logical fallacy detection . fallacious arguments can lead to disagreements, conflicts, endless debates, and a lack of consensus .
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How Susceptible Are LLMs to Logical Fallacies? (2024.lrec-main)

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Challenge: Recent studies have focused on LLMs' reasoning abilities, but their rational thinking capacity is not as robust as that of other NLP downstream tasks.
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Reason from Fallacy: Enhancing Large Language Models’ Logical Reasoning through Logical Fallacy Understanding (2024.findings-naacl)

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Challenge: Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but struggle with some more complex reasoning tasks including logical reasoning.
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Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation (2026.acl-long)

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Challenge: Existing systems that detect logical fallacies in public discourse do not help people recognize them independently.
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