Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation
About
Identifying logical fallacies in everyday discourse is challenging for many people. This challenge is amplified in the era of Large Language Models (LLMs), where malicious agents can deploy fallacious arguments to disseminate misinformation at scale. In this work, we explore the potential of LLMs as part of the solution. We introduce LFTutor, an intelligent tutoring system which uses LLMs to tutor laypeople and help them learn about logical fallacies. LFTutor integrates intent-driven Socratic questioning and critical argumentation principles to actively engage learners to reflect on their reasoning. Through both automatic and human evaluations, we demonstrate that LFTutor significantly outperforms baseline LLMs lacking these pedagogical strategies. This work highlights the promise of combining LLMs with pedagogical scaffolding to foster critical thinking and argument literacy in the age of AI.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Logical Fallacy Tutoring | Elec2Deb20 (normal students) | Divergence84.6 | 3 | |
| Dialogue Evaluation | Elec2Deb20 Normal Students 1.0 (test) | Divergence86 | 2 | |
| Dialogue Evaluation | Elec2Deb20 (normal students) | Divergence71 | 2 | |
| Logical Fallacy Tutoring | Elec2Deb20 Human Evaluation (pilot study) | Divergence3.3 | 2 | |
| Logical Fallacy Tutoring | Elec2Deb20 adversarial student | Divergence Rate38 | 2 |