analiza aiqbench
TruthSplit introduces a novel framework for multi-perspective argument analysis that explicitly conditions large language model reasoning on structured worldview profiles to assess logical and normative consistency. By extracting claims and premises to visualize divergence in value conflicts, the system addresses a critical gap in existing tools that often treat background knowledge as implicit. This approach offers a significant step forward for building more robust, context-aware AI systems capable of handling complex, subjective reasoning tasks.
TruthSplit: Operationalizing Conditional Validity in Arguments Through Multi-Perspective Reasoning
arXiv:2606.09251v1 Announce Type: new 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.
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.
Demo paper. To appear at ACL 2026
Computation and Language (cs.CL)
arXiv:2606.09251 [cs.CL]
(or arXiv:2606.09251v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.09251 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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