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

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 corpusspecific 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.* Equal contribution.