Summary of A Logical Fallacy-informed Framework For Argument Generation, by Luca Mouchel et al.
A Logical Fallacy-Informed Framework for Argument Generation
by Luca Mouchel, Debjit Paul, Shaobo Cui, Robert West, Antoine Bosselut, Boi Faltings
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed FIPO framework aims to improve the argument generation capabilities of Large Language Models (LLMs) by incorporating a fallacy-informed approach. The method leverages preference optimization methods to steer LLMs towards logically sound arguments, with a classification loss component that captures fine-grained information on fallacy types. Experimental results demonstrate a significant reduction in fallacy errors, outperforming fine-tuned baselines and other preference optimization methods like DPO. This highlights the importance of ensuring models are aware of logical fallacies for effective argument generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models have great natural language processing skills, but they struggle to make sound arguments that aren’t misleading. To fix this issue, scientists created a new way called FIPO (Fallacy-Informed Preference Optimization). It helps the models create good arguments by using special methods to guide them away from false information. In tests with argument datasets, FIPO reduced mistakes by up to 17.5%. People also liked the quality of the generated arguments much better than others. |
Keywords
* Artificial intelligence * Classification * Natural language processing * Optimization
