🤖 AI Summary
Existing argument analysis tools struggle to evaluate the conditional validity of arguments across diverse worldviews. This work proposes a multi-perspective reasoning framework that operationalizes conditional validity for the first time by integrating structured worldview modeling, three-tier natural language inference, and conditional reasoning with large language models. The system automatically identifies value conflicts and assumption gaps, generating perspective-specific explanations. It further supports interactive visualization to effectively reveal differences in logical and normative coherence of the same argument under pluralistic value systems, thereby enabling users to explore multidimensional interpretations of complex arguments.
📝 Abstract
We present TruthSplit, an interactive system for multi-perspective argument analysis. Existing argumentation tools typically analyze properties of the argument itself, such as structure, quality, stance, or persuasiveness, while leaving perspective-specific background knowledge implicit. TruthSplit addresses this gap by supporting an exploratory analysis of how the same claim can lead to different conclusions when interpreted through worldview-specific values, assumptions, and conceptual definitions. We refer to this perspective-dependent analysis as conditional validity. Given an input argumentative text, TruthSplit extracts claims and premises, applies a three-layer natural language inference (NLI) approach to assess both logical and worldview-specific normative consistency, and conditions large language model (LLM) reasoning on structured worldview profiles that encode core values and decision principles. The system then generates perspective-specific interpretations, identifies value conflicts and assumption gaps, and visualizes divergence through interactive analytical interfaces.