ArgLLM-App: An Interactive System for Argumentative Reasoning with Large Language Models

Adam Dejl; Deniz Gorur; Francesca Toni

cs.CLFeb 27, 2026

ArgLLM-App: An Interactive System for Argumentative Reasoning with Large Language Models

Authors: Adam Dejl, Deniz Gorur, Francesca Toni

Organizations: Imperial College London, London, United Kingdom

Abstract

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.

Figures & tables

Figure 1. Output of ArgLLM-App for claim “Ukraine will join the EU before 2030”, with settings as in Figure 2 and document-based QBAF generation (see the PDF document indication at the bottom of the chat). In the QBAF, the bottom left argument has been selected for addition of a supporter at depth 3. The abstract graph at the bottom right shows the QBAF structure.

Figure 2. Settings: in addition to the API key for access to the base LLM, the user can configure the gradual semantics, depth and breadth to be used by ArgLLM-App.

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Argumentation for Explainable and Globally Contestable Decision Support with LLMs

Adam Dejl, Matthew Williams, Francesca Toni

Department of Computing, Imperial College London · Department of Surgery & Cancer, Imperial College London

AI-Assisted Decision Making Explainable Artificial Intelligence LLM Decision-Making Computational Argumentation Clinical Decision Support

Open paper AskPDF arXiv

Large language models (LLMs) exhibit strong general capabilities, but their deployment in high-stakes domains is hindered by their opacity and unpredictability. Recent work has taken meaningful steps towards addressing these issues by augmenting LLMs with post-hoc reasoning based on computational argumentation, providing faithful explanations and enabling users to contest incorrect decisions. However, this paradigm is limited to pre-defined binary choices and only supports local contestation for specific instances, leaving the underlying decision logic unchanged and prone to repeated mistakes. In this paper, we introduce ArgEval, a framework that shifts from instance-specific reasoning to structured evaluation of general decision options. Rather than mining arguments solely for individual cases, ArgEval systematically maps task-specific decision spaces, builds corresponding option ontologies, and constructs general argumentation frameworks (AFs) for each option. These frameworks can then be instantiated to provide explainable recommendations for specific cases while still supporting global contestability through modification of the shared AFs. We investigate the effectiveness of ArgEval on treatment recommendation for glioblastoma, an aggressive brain tumour, and show that it can produce explainable guidance aligned with clinical practice.

Abstract

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