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October 13, 2025Open Access

Joint Effects of Argumentation Theory, Audio Modality and Data Enrichment on LLM-Based Fallacy Classification

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Authors

HZHongxu ZhouHWHylke WesterdijkKIKhondoker Ittehadul Islam

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Overview

This observational analysis evaluates fallacy classification in political debates, suggesting context and emotional tone may confuse large language models.

Key Points

  • LLM performance in fallacy classification is affected by emotional tone metadata, which can lower accuracy.
  • Theoretical prompting frameworks improved interpretability but sometimes resulted in worse logical reasoning outcomes.
  • Using enhanced prompts did not consistently yield better performance compared to basic prompts in classifying fallacies.
  • The introduction of additional context and emotional tone often diluted the attention of the LLMs, complicating their reasoning.

Cite This Study

Zhou et al. (2025) studied this question.

synapsesocial.com/papers/68ecfebf950606aabec09383https://doi.org/10.48550/arxiv.2509.11127
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Also Consider

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  1. 1Can LLMs Judge Debates? Evaluating Non-Linear Reasoning via Argumentation Theory Semantics2025
  2. 2A comprehensive study of LLM-based argument classification: from Llama through DeepSeek to GPT-5.22026
  3. 3Can formal argumentative reasoning enhance LLMs performances?2024 · 1 citations
  4. 4Can Language Models Recognize Convincing Arguments?2024 · 2 citations
  5. 5Audio Entailment: Assessing Deductive Reasoning for Audio Understanding2024