@inproceedings{kargupta-etal-2025-beyond,
title = "Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims",
author = "Kargupta, Priyanka and
Tian, Runchu and
Han, Jiawei",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1434/",
pages = "29664--29679",
ISBN = "979-8-89176-251-0",
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."
}
Markdown (Informal)
[Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims](https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1434/) (Kargupta et al., ACL 2025)
ACL