The diversity of knowledge encoded in large language models (LLMs) and their ability to apply this knowledge zero-shot in a range of settings makes them a promising candidate for use in decision-making. However, they are currently limited by their inability to reliably provide outputs which are explainable and contestable. In this paper, we attempt to reconcile these strengths and weaknesses by introducing a method for supplementing LLMs with argumentative reasoning. Concretely, we introduce argumentative LLMs, a method utilising LLMs to construct argumentation frameworks, which then serve as the basis for formal reasoning in decision-making. The interpretable nature of these argumentation frameworks and formal reasoning means that any decision made by the supplemented LLM may be naturally explained to, and contested by, humans. We demonstrate the effectiveness of argumentative LLMs experimentally in the decision-making task of claim verification. We obtain results that are competitive with, and in some cases surpass, comparable state-of-the-art techniques.

翻译：大型语言模型（LLMs）所编码知识的多样性，以及其在多种场景中零样本应用这些知识的能力，使其成为决策领域的潜在候选方案。然而，当前LLMs受限于无法可靠提供可解释且可争议的输出。本文尝试通过引入一种基于论证推理增强LLMs的方法来调和这些优势与不足。具体而言，我们提出论证式大语言模型（argumentative LLMs），该方法利用LLMs构建论证框架，进而作为决策中形式化推理的基础。这些论证框架与形式化推理的可解释性特征，使得经增强后的LLMs所做的任何决策均能自然地由人类理解并可提出质疑。我们通过索赔验证这一决策任务实验验证了论证式大语言模型的有效性。结果表明，该方法取得与现有最优技术相当甚至在某些场景下更优的性能。