TruthSplit: Operationalizing Conditional Validity in Arguments Through Multi-Perspective Reasoning
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Natural Language InferenceSimilar Papers 제목 키워드 기반
Developing an argument annotation scheme based on a semantic classification of arguments
Corpora of argumentative discourse are commonly analyzed in terms of argumentative units, consisting of claims and premises. Both argument detection and classification are complex discourse processing tasks. Our paper in…
ClassificationMany-valued Argumentation, Conditionals and a Probabilistic Semantics for Gradual Argumentation
In this paper we propose a general approach to define a many-valued preferential interpretation of gradual argumentation semantics. The approach allows for conditional reasoning over arguments and boolean combination of …
Mitigating Content Effects on Reasoning in Language Models through Fine-Grained Activation Steering
Large language models (LLMs) frequently demonstrate reasoning limitations, often conflating content plausibility (i.e., material inference) with logical validity (i.e., formal inference). This can result in biased infere…
Language ModelingLanguage ModellingApproximation to Object Conditional Validity with Inductive Conformal Predictors
Conformal predictors are machine learning algorithms that output prediction sets that have a guarantee of marginal validity for finite samples with minimal distributional assumptions. This is a property that makes confor…
BIG-bench Machine LearningObjectPredictionPrediction Intervals+1Towards Ranking-based Semantics for Abstract Argumentation using Conditional Logic Semantics
We propose a novel ranking-based semantics for Dung-style argumentation frameworks with the help of conditional logics. Using an intuitive translation for an argumentation framework to generate conditionals, we can apply…
Abstract ArgumentationTranslation