A Logical Fallacy-Informed Framework for Argument Generation

TL;DR

This paper presents FIPO, a framework that improves the logical soundness of argument generation by LLMs through fallacy-aware preference optimization, reducing fallacy errors and enhancing argument quality.

Contribution

Introducing FIPO, a fallacy-informed framework that leverages classification loss and preference optimization to generate logically sound arguments with fewer fallacies.

Findings

Reduces fallacy errors by up to 17.5%

Outperforms fine-tuned baselines and DPO in human evaluations

Enhances argument quality significantly

Abstract

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.