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

Priyanka Kargupta, Runchu Tian, Jiawei Han


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.
Anthology ID:
2025.acl-long.1434
Volume:
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
29664–29679
Language:
URL:
https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1434/
DOI:
Bibkey:
Cite (ACL):
Priyanka Kargupta, Runchu Tian, and Jiawei Han. 2025. Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 29664–29679, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims (Kargupta et al., ACL 2025)
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PDF:
https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1434.pdf