A Logical Fallacy-Informed Framework for Argument Generation

2024-08-07

arXiv.org

AI Summary

Large language models (LLMs) frequently generate fallacious arguments, compromising logical validity and producing misleading outputs. To address this, we propose Fallacy-Informed Preference Optimization (FIPO), the first framework to integrate fine-grained logical fallacy classification loss directly into reward-model-free preference optimization objectives—such as DPO variants—enabling end-to-end fallacy suppression. FIPO jointly optimizes fallacy detection and human preference alignment by unifying supervised fine-tuning with multi-class fallacy classification, thereby enhancing logical rigor. Evaluated across multiple argumentation benchmarks, FIPO reduces fallacy rates by up to 17.5% compared to baselines. Human evaluations demonstrate statistically significant improvements over standard supervised fine-tuning and DPO, confirming both the efficacy and novelty of fallacy-aware modeling for improving argument quality.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Argumentation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• LLMs struggle with logical argument generation.
• FIPO framework reduces fallacy errors effectively.
• Ensuring model awareness of logical fallacies.

Innovation

• Fallacy-informed framework
• Preference optimization methods
• Classification loss integration