A Logical Fallacy-Informed Framework for Argument Generation
This addresses the issue of logical fallacies in AI-generated arguments, which is important for applications like misinformation prevention, though it appears incremental as it builds on existing preference optimization methods.
The paper tackles the problem of LLMs generating logically unsound arguments, which risks spreading misinformation, by introducing FIPO, a fallacy-informed framework that reduces fallacy errors by up to 17.5% and significantly improves argument quality over baselines.
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.