Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims

2025-06-12

AI Summary

Scientific and political claims often resist binary truth-value assignment (true/false), necessitating fine-grained, multidimensional verification. Method: This paper proposes a hierarchical claim decomposition framework that automatically breaks down complex claims (e.g., “Vaccine A outperforms Vaccine B”) into verifiable dimensions (e.g., efficacy, safety) and sub-dimensions, leveraging retrieval-augmented generation (RAG) for claim-driven hierarchical retrieval and generation. It innovatively integrates hierarchical prompt engineering, structured claim modeling, multi-perspective stance classification (support/neutral/oppose), and statistical aggregation. Contribution/Results: The framework achieves end-to-end structural discovery, sub-dimension expansion, and quantitative stance assessment. Evaluated on a novel, multi-domain dataset curated by the authors, it significantly outperforms baseline methods. Human evaluation confirms its strong structural integrity, comprehensive stance coverage, and high interpretability and credibility of explanations.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Claims made by individuals or entities are oftentimes nuanced and cannot be clearly labeled as entirely"true"or"false"-- as is frequently the case with scientific and political claims. However, a claim (e.g.,"vaccine A is better than vaccine B") can be dissected into its integral aspects and sub-aspects (e.g., efficacy, safety, distribution), which are individually easier to validate. This enables a more comprehensive, structured response that provides a well-rounded perspective on a given problem while also allowing the reader to prioritize specific angles of interest within the claim (e.g., safety towards children). Thus, we propose ClaimSpect, a retrieval-augmented generation-based framework for automatically constructing a hierarchy of aspects typically considered when addressing a claim and enriching them with corpus-specific perspectives. This structure hierarchically partitions an input corpus to retrieve relevant segments, which assist in discovering new sub-aspects. Moreover, these segments enable the discovery of varying perspectives towards an aspect of the claim (e.g., support, neutral, or oppose) and their respective prevalence (e.g.,"how many biomedical papers believe vaccine A is more transportable than B?"). We apply ClaimSpect to a wide variety of real-world scientific and political claims featured in our constructed dataset, showcasing its robustness and accuracy in deconstructing a nuanced claim and representing perspectives within a corpus. Through real-world case studies and human evaluation, we validate its effectiveness over multiple baselines.

Problem

• Analyzing nuanced claims beyond binary true/false labels
• Hierarchically dissecting claims into verifiable sub-aspects
• Retrieving corpus-specific perspectives on claim aspects

Innovation

• Hierarchical analysis of nuanced claims
• Retrieval-augmented generation framework
• Corpus-specific perspective enrichment