@inproceedings{mouchel-etal-2025-logical,
title = "A Logical Fallacy-Informed Framework for Argument Generation",
author = "Mouchel, Luca and
Paul, Debjit and
Cui, Shaobo and
West, Robert and
Bosselut, Antoine and
Faltings, Boi",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.374/",
doi = "10.18653/v1/2025.naacl-long.374",
pages = "7296--7314",
ISBN = "979-8-89176-189-6",
abstract = "Despite the remarkable performance of large language models (LLMs), they still struggle with generating logically sound arguments, resulting in potential risks such as spreading misinformation. An important factor contributing to LLMs' suboptimal performance in generating coherent arguments is their oversight of logical fallacies. To address this issue, we introduce fallacy-informed preference optimization (FIPO) that helps steer LLMs toward generating logically sound arguments. FIPO includes a classification loss to capture the fine-grained information on fallacy types. Our results on argument generation tasks show that FIPO reduces the fallacy errors by up to 17.5{\%}. Furthermore, our human evaluation results reveal that the quality of the arguments generated by our method significantly outperforms the fine-tuned baselines and other preference optimization methods, such as DPO. These findings highlight the importance of ensuring models are aware of logical fallacies for effective argument generation."
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<abstract>Despite the remarkable performance of large language models (LLMs), they still struggle with generating logically sound arguments, resulting in potential risks such as spreading misinformation. An important factor contributing to LLMs’ suboptimal performance in generating coherent arguments is their oversight of logical fallacies. To address this issue, we introduce fallacy-informed preference optimization (FIPO) that helps steer LLMs toward generating logically sound arguments. FIPO includes a classification loss to capture the fine-grained information on fallacy types. Our results on argument generation tasks show that FIPO reduces the fallacy errors by up to 17.5%. Furthermore, our human evaluation results reveal that the quality of the arguments generated by our method significantly outperforms the fine-tuned baselines and other preference optimization methods, such as DPO. These findings highlight the importance of ensuring models are aware of logical fallacies for effective argument generation.</abstract>
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%0 Conference Proceedings
%T A Logical Fallacy-Informed Framework for Argument Generation
%A Mouchel, Luca
%A Paul, Debjit
%A Cui, Shaobo
%A West, Robert
%A Bosselut, Antoine
%A Faltings, Boi
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F mouchel-etal-2025-logical
%X Despite the remarkable performance of large language models (LLMs), they still struggle with generating logically sound arguments, resulting in potential risks such as spreading misinformation. An important factor contributing to LLMs’ suboptimal performance in generating coherent arguments is their oversight of logical fallacies. To address this issue, we introduce fallacy-informed preference optimization (FIPO) that helps steer LLMs toward generating logically sound arguments. FIPO includes a classification loss to capture the fine-grained information on fallacy types. Our results on argument generation tasks show that FIPO reduces the fallacy errors by up to 17.5%. Furthermore, our human evaluation results reveal that the quality of the arguments generated by our method significantly outperforms the fine-tuned baselines and other preference optimization methods, such as DPO. These findings highlight the importance of ensuring models are aware of logical fallacies for effective argument generation.
%R 10.18653/v1/2025.naacl-long.374
%U https://aclanthology.org/2025.naacl-long.374/
%U https://doi.org/10.18653/v1/2025.naacl-long.374
%P 7296-7314
Markdown (Informal)
[A Logical Fallacy-Informed Framework for Argument Generation](https://aclanthology.org/2025.naacl-long.374/) (Mouchel et al., NAACL 2025)
ACL
- Luca Mouchel, Debjit Paul, Shaobo Cui, Robert West, Antoine Bosselut, and Boi Faltings. 2025. A Logical Fallacy-Informed Framework for Argument Generation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7296–7314, Albuquerque, New Mexico. Association for Computational Linguistics.