2025 EMNLP EMNLP 2025

Hybrid Intelligence for Logical Fallacy Detection

Abstract

AbstractThis study investigates the impact of Hybrid Intelligence (HI) on improving the detection of logical fallacies, addressing the pressing challenge of misinformation prevalent across communication platforms. Employing a between-subjects experimental design, the research compares the performance of two groups: one relying exclusively on human judgment and another supported by an AI assistant. Participants evaluated a series of statements, with the AI-assisted group utilizing a custom ChatGPT-based chatbot that provided real-time hints and clarifications. The findings reveal a significant improvement in fallacy detection with AI support, increasing from an F1-score of 0.76 in the human-only group to 0.90 in the AI-assisted group. Despite this enhancement, both groups struggled to accurately identify non-fallacious statements, highlighting the need to further refine how AI assistance is leveraged.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🧭 Keyword Pioneer — chatbot assistance
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning