Beyond True or False: Retrieval-Augmented Hierarchical Analysis of
  Nuanced Claims
Authors: name: Priyanka Kargupta, name: Runchu Tian, name: Jiawei Han

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.