The development of Large Language Models (LLMs) has brought impressive performances on mitigation strategies against misinformation, such as counterargument generation. However, LLMs are still seriously hindered by outdated knowledge and by their tendency to generate hallucinated content. In order to circumvent these issues, we propose a new task, namely, Critical Questions Generation, consisting of processing an argumentative text to generate the critical questions (CQs) raised by it. In argumentation theory CQs are tools designed to lay bare the blind spots of an argument by pointing at the information it could be missing. Thus, instead of trying to deploy LLMs to produce knowledgeable and relevant counterarguments, we use them to question arguments, without requiring any external knowledge. Research on CQs Generation using LLMs requires a reference dataset for large scale experimentation. Thus, in this work we investigate two complementary methods to create such a resource: (i) instantiating CQs templates as defined by Walton's argumentation theory and (ii), using LLMs as CQs generators. By doing so, we contribute with a procedure to establish what is a valid CQ and conclude that, while LLMs are reasonable CQ generators, they still have a wide margin for improvement in this task.

翻译：大型语言模型（LLMs）的发展在应对错误信息的缓解策略（如反论证生成）方面取得了令人瞩目的性能。然而，LLMs仍受到知识陈旧和倾向于生成幻觉内容的严重制约。为规避这些问题，我们提出一项新任务——关键问题生成，即处理论证性文本以生成其所引发的关键问题。在论证理论中，关键问题是旨在通过指出论证可能缺失的信息来揭示其盲点的工具。因此，我们并非试图部署LLMs来生成知识渊博且相关的反论证，而是利用它们来质疑论证，且无需任何外部知识。基于LLMs的关键问题生成研究需要大规模实验的参考数据集。为此，本研究探索了两种互补的资源构建方法：（一）实例化沃尔顿论证理论中定义的关键问题模板；（二）使用LLMs作为关键问题生成器。通过这项工作，我们贡献了一套界定有效关键问题的流程，并得出结论：虽然LLMs是合理的关键问题生成器，但在此任务中仍有广阔的改进空间。