Argumentative LLMs (ArgLLMs) are an existing approach leveraging Large Language Models (LLMs) and computational argumentation for decision-making, with the aim of making the resulting decisions faithfully explainable to and contestable by humans. Here we propose a web-based system implementing ArgLLM-empowered agents for binary tasks. ArgLLM-App supports visualisation of the produced explanations and interaction with human users, allowing them to identify and contest any mistakes in the system's reasoning. It is highly modular and enables drawing information from trusted external sources. ArgLLM-App is publicly available at https://argllm.app, with a video demonstration at https://youtu.be/vzwlGOr0sPM.

翻译：论证性大语言模型（ArgLLMs）是一种现有方法，它结合大语言模型（LLMs）与计算论证技术进行决策，旨在使最终决策能够忠实可解释且可供人类质疑。本文提出一个基于网络的系统，该系统实现了由ArgLLM赋能的智能体以处理二元任务。ArgLLM-App支持对生成的解释进行可视化展示，并允许与人类用户交互，使用户能够识别并质疑系统推理中的任何错误。该系统具有高度模块化特性，并能从可信外部来源获取信息。ArgLLM-App已在 https://argllm.app 公开提供，演示视频可见 https://youtu.be/vzwlGOr0sPM。