| Challenge: | Argument generation is crucial in daily life and has numerous online and offline applications. |
| Approach: | They propose a fallacy-informed preference optimization that includes a classification loss to capture the fine-grained information on fallacy types to help LLMs generate logically sound arguments. |
| Outcome: | The proposed method reduces fallacy errors by 17.5% on argument generation tasks and outperforms fine-tuned baselines and other preference optimization methods, such as DPO. |
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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. |
| Approach: | They propose five concrete tasks from three cognitive dimensions of WHAT, WHY, and HOW to evaluate LLMs’ capability of logical fallacy understanding. |
| Outcome: | The proposed dataset can be used to evaluate LLMs’ LFU capability and to fine-tune LLM models to obtain significantly enhanced performance on logical reasoning. |
Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-Aware Prompt Formulation (2025.findings-naacl)
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| Challenge: | Recent large language models have demonstrated improved processing of complex language, but detecting logical fallacies remains a challenge. |
| Approach: | They propose a prompt formulation approach for logical fallacy detection that integrates contextual information into input text and queries for validity within the argument’s context. |
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Improving Argument Effectiveness Across Ideologies using Instruction-tuned Large Language Models (2024.findings-emnlp)
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| Challenge: | a study finds that different political ideologies hold different worldviews, which leads to contentious debates . argument effectiveness is improved by using instruction-tuned large language models . |
| Approach: | They propose to use instruction-tuned large language models to turn ineffective arguments into effective arguments for people with certain ideologies. |
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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. |
| Approach: | They propose a diagnostic benchmark to assess the robustness of Large Language Models against logical fallacies by comparing their performance against a scenario where the persuader employs logical fallsacie. |
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Flee the Flaw: Annotating the Underlying Logic of Fallacious Arguments Through Templates and Slot-filling (2024.emnlp-main)
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Irfan Robbani, Paul Reisert, Surawat Pothong, Naoya Inoue, Camélia Guerraoui, Wenzhi Wang, Shoichi Naito, Jungmin Choi, Kentaro Inui
| 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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Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection (2026.eacl-long)
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| Challenge: | Existing computational approaches focus on logical structures of fallacies and argumentation schemes, ignoring the emotional dimension of argumentation. |
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Conclusion-based Counter-Argument Generation (2023.eacl-main)
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| Challenge: | Existing work on the automatic generation of natural language counter-arguments does not address the relation to the conclusion, possibly because many arguments leave their conclusion implicit. |
| Approach: | They propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument. |
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Improving LLM Generations via Fine-Grained Self-Endorsement (2024.findings-acl)
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| Challenge: | Recent large language models (LLMs) have demonstrated remarkable capabilities but can still fail frequently on knowledge-intensive tasks. |
| Approach: | They propose a self-endorsement framework that leverages fine-grained fact-level comparisons across multiple sampled responses. |
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Argument Generation with Retrieval, Planning, and Realization (P19-1)
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| Challenge: | a novel argument generation framework is used to generate counter-arguments . CANDELA uses a text planning decoder to retrieve arguments of different perspectives . |
| Approach: | They propose a powerful retrieval system and a novel two-step argument generation framework . they use a retrieval-based retrieval platform indexed with 12 million articles from Wikipedia . |
| Outcome: | The proposed framework yields higher BLEU, ROUGE, and METEOR scores than state-of-the-art models. |
Argument Mining with Fine-Tuned Large Language Models (2025.coling-main)
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| Challenge: | Argument Mining (AM) pipelines use fine-tuned large language models (LLMs) . initial approaches employ supervised machine learning algorithms, such as Maximum Entropy classifiers and Logistic Regressions. |
| Approach: | They propose to model the three main AM sub-tasks as text generation tasks and fine-tune eight popular quantized and non-quantized large language models (LLMs) on the benchmark PE, AbstRCT, and CDCP datasets. |
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