DeceptionAnalyser：一种基于Web的AI工具，利用论证方案和大型语言模型进行结构化欺骗分析
DeceptionAnalyser: A Web-Based AI Tool for Performing Structured Deception Analysis with Argumentation Schemes and LLMs
- University of Lincoln（林肯大学）
- University of the Basque Country（巴斯克大学）
- University of Liverpool（利物浦大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出十个论证方案及DeceptionAnalyser工具，结合大型语言模型进行结构化欺骗分析，通过前提提取和关键问题评估实现透明可解释的欺骗检测，并在多个模型上验证了可靠性。
AI中文摘要：
欺骗在情报行动中扮演核心角色，然而，若无推理模式和认知操纵方面的专业知识，对欺骗进行系统分析仍然困难。例如，在计算论证领域，目前尚无支持统计验证的方案级真实语料库。在本文中，我们通过引入一组十个论证方案来解决这一问题，这些方案旨在建模不同形式的欺骗，每个方案均配有结构化前提和关键问题。借此，我们推出了首个专门用于欺骗分析的论证方案库，为系统建模和分析叙事文本中的欺骗提供了结构化基础。随后，我们介绍了DeceptionAnalyser，一种基于浏览器的工具，通过结合基于大型语言模型的前提提取与关键问题驱动的评估的两阶段方法来实现这些方案。我们的目标是为分析叙事文本中的欺骗性推理提供概念和方法论基础。这正是我们在本文中所要解决的问题，通过展示结构化论证理论和AI辅助分析如何支持对潜在欺骗进行透明、可解释的评估。由于这些方案旨在标记需要审查的主张而非输出欺骗裁决，我们不对分类准确性进行基准测试；相反，我们通过测量该工具在十个当代大型语言模型及重复运行中的前提和结论评估一致性来评估方法的可靠性。我们发现，对于明确的欺骗，方案检测高度稳定，而对于更模糊的情报风格叙事，检测会以可解释的方式优雅地退化。
英文摘要：
Deception plays a central role in Intelligence operations, yet it remains difficult to analyse systematically without expert knowledge of reasoning patterns and cognitive manipulation. In computational argumentation, for instance, no scheme-level ground-truth corpora currently exist to support statistical validation. In this paper, we address this by introducing a set of ten argument schemes designed to model distinct forms of deception, each accompanied by structured premises and critical questions. In doing so, we introduce the first dedicated library of argumentation schemes specifically designed for deception analysis, providing a structured foundation for systematically modelling and analysing deception in narrative text. We then present \textit{DeceptionAnalyser}, a browser-based tool that implements these schemes through a two-stage methodology combining LLM-based premise extraction with critical-question-driven evaluation. Our aim is to provide a conceptual and methodological foundation for analysing deceptive reasoning in narrative text. This is precisely what we address in this paper by demonstrating how structured argumentation theory and AI-assisted analysis can support transparent, explainable assessments of potential deception. Because the schemes are designed to flag claims for scrutiny rather than to output a deception verdict, we do not benchmark classification accuracy; instead, we assess the \emph{reliability} of the methodology by measuring the consistency of the tool's premise and conclusion assessments across ten contemporary large language models and repeated runs. We find that scheme detection is highly stable for clear-cut deception and degrades gracefully, in interpretable ways, on more ambiguous intelligence-style narratives.
