TruthSplit：通过多视角推理实现论证中的条件有效性操作化
TruthSplit: Operationalizing Conditional Validity in Arguments Through Multi-Perspective Reasoning
AI总结：
提出TruthSplit系统，通过三层自然语言推理和结构化世界观档案，实现基于不同视角的论证条件有效性分析，识别价值冲突与假设差异。
摘要：
我们提出TruthSplit，一个用于多视角论证分析的交互式系统。现有的论证工具通常分析论证本身的属性，如结构、质量、立场或说服力，而将特定视角的背景知识隐含起来。TruthSplit通过支持探索性分析来填补这一空白，即当通过世界观特定的价值观、假设和概念定义来解释时，同一主张如何导致不同的结论。我们将这种依赖于视角的分析称为条件有效性。给定输入的论证文本，TruthSplit提取主张和前提，应用三层自然语言推理（NLI）方法来评估逻辑和世界观特定的规范性一致性，并将大语言模型（LLM）推理条件化为编码核心价值观和决策原则的结构化世界观档案。然后，系统生成特定视角的解释，识别价值冲突和假设差距，并通过交互式分析界面可视化分歧。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
