mlsanityData to 7 Oct 2026

A Logical Fallacy-Informed Framework for Argument Generation

arXiv 2408.03618 ↗First posted 7 Aug 2024cs.CL cs.AI cs.LGcited by 7

On the map: LLM alignment / RLHF / preference optimisation → Process reward models for reasoning

Abstract (arXiv)

Despite the remarkable performance of Large Language Models (LLMs) in natural language processing tasks, they still struggle with generating logically sound arguments, resulting in potential risks such as spreading misinformation. To address this issue, we introduce FIPO, a fallacy-informed framework that leverages preference optimization methods to steer LLMs toward logically sound arguments. FIPO includes a classification loss, to capture the fine-grained information on fallacy types. Our results on argumentation datasets show that our method reduces the fallacy errors by up to 17.5%. Furthermore, our human evaluation results indicate that the quality of the generated arguments by our method significantly outperforms the fine-tuned baselines, as well as other preference optimization methods, such as DPO. These findings highlight the importance of ensuring models are aware of logical fallacies for effective argument generation. Our code is available at github.com/lucamouchel/Logical-Fallacies.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Process reward models for reasoning, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 24 of its 49 references that are in the map. First posted on arXiv 7 Aug 2024.

Builds on

24 of 49 references.

References · 24

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12 more
  1. 2024Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translationcited by 390
  2. 2023Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervisioncited by 318
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  12. 2023ArgU: A Controllable Factual Argument Generatorcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 2.

7 papers.

  1. 2025Do Emotions Really Affect Argument Convincingness? A Dynamic Approach with LLM-based Manipulation Checkscited by 6
  2. 2024Gaps or Hallucinations? Gazing into Machine-Generated Legal Analysis for Fine-grained Text Evaluationscited by 4
  3. 2025Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detectioncited by 2
  4. 2024Nuance Matters: Probing Epistemic Consistency in Causal Reasoningcited by 1
  5. 2025Joint Effects of Argumentation Theory, Audio Modality and Data Enrichment on LLM-Based Fallacy Classificationcited by 1
  6. 2026ArgBench: Benchmarking LLMs on Computational Argumentation Taskscited by 1
  7. 2026Can One-sided Arguments Lead to Response Change in Large Language Models?cited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-Aware Prompt Formulation
  2. 2025Large Language Models in Argument Mining: A Survey
  3. 2026Argument Quality Assessment with Large Language Models: A Pairwise Bradley-Terry Approach
  4. 2024"Let's Argue Both Sides": Argument Generation Can Force Small Models to Utilize Previously Inaccessible Reasoning Capabilities
  5. 2024Boosting Logical Fallacy Reasoning in LLMs via Logical Structure Tree
  6. 2022Logical Fallacy Detection
  7. 2025A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1
  8. 2025Can LLMs Judge Debates? Evaluating Non-Linear Reasoning via Argumentation Theory Semantics