Assisted Debate Builder with Large Language Models

In recent years, there has been a lot of research in artificial intelligence, focusing on leveraging argumentation theory for non-monotonic reasoning [1, 2]. Starting with Dung's seminal work [3], many researchers have considered abstract argumentation frameworks, composed of a set of arguments and a binary attack relation between them, and created many semantics for tasks such as computing accepted sets of arguments [4, 5] or rank arguments [6, 7, 8]. This abstract argumentation framework was extended with many features such as supports [9, 10, 11], sets of attacking arguments [12, 13], or probabilities [14] among others. However, one important question that remained was: "Where do argumentation frameworks come from in real-life settings?". While there are some pieces of evidence that the fundamental aspects of abstract argumentation frameworks have links with human reasoning [15, 16], humans debates or natural language texts are not always written as arguments and the relation between arguments is not always clear, even for experts [17]. The question of the origin of argumentation frameworks is crucial to facilitate the application of argumentation theory semantics in real-world contexts.