@inproceedings{roy-dipta-ferraro-2025-may,
title = "If We May De-Presuppose: Robustly Verifying Claims through Presupposition-Free Question Decomposition",
author = "Roy Dipta, Shubhashis and
Ferraro, Francis",
editor = "Frermann, Lea and
Stevenson, Mark",
booktitle = "Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025)",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.starsem-1.20/",
pages = "253--266",
ISBN = "979-8-89176-340-1",
abstract = "Prior work has shown that presupposition in generated questions can introduce unverified assumptions, leading to inconsistencies in claim verification. Additionally, prompt sensitivity remains a significant challenge for large language models (LLMs), resulting in performance variance as high as **3{--}6{\%}**. While recent advancements have reduced this gap, our study demonstrates that prompt sensitivity remains a persistent issue. To address this, we propose a structured and robust claim verification framework that reasons through presupposition-free, decomposed questions. Extensive experiments across multiple prompts, datasets, and LLMs reveal that even state-of-the-art models remain susceptible to prompt variance and presupposition. Our method consistently mitigates these issues, achieving up to a **2{--}5{\%}** improvement."
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<abstract>Prior work has shown that presupposition in generated questions can introduce unverified assumptions, leading to inconsistencies in claim verification. Additionally, prompt sensitivity remains a significant challenge for large language models (LLMs), resulting in performance variance as high as **3–6%**. While recent advancements have reduced this gap, our study demonstrates that prompt sensitivity remains a persistent issue. To address this, we propose a structured and robust claim verification framework that reasons through presupposition-free, decomposed questions. Extensive experiments across multiple prompts, datasets, and LLMs reveal that even state-of-the-art models remain susceptible to prompt variance and presupposition. Our method consistently mitigates these issues, achieving up to a **2–5%** improvement.</abstract>
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%0 Conference Proceedings
%T If We May De-Presuppose: Robustly Verifying Claims through Presupposition-Free Question Decomposition
%A Roy Dipta, Shubhashis
%A Ferraro, Francis
%Y Frermann, Lea
%Y Stevenson, Mark
%S Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-340-1
%F roy-dipta-ferraro-2025-may
%X Prior work has shown that presupposition in generated questions can introduce unverified assumptions, leading to inconsistencies in claim verification. Additionally, prompt sensitivity remains a significant challenge for large language models (LLMs), resulting in performance variance as high as **3–6%**. While recent advancements have reduced this gap, our study demonstrates that prompt sensitivity remains a persistent issue. To address this, we propose a structured and robust claim verification framework that reasons through presupposition-free, decomposed questions. Extensive experiments across multiple prompts, datasets, and LLMs reveal that even state-of-the-art models remain susceptible to prompt variance and presupposition. Our method consistently mitigates these issues, achieving up to a **2–5%** improvement.
%U https://aclanthology.org/2025.starsem-1.20/
%P 253-266
Markdown (Informal)
[If We May De-Presuppose: Robustly Verifying Claims through Presupposition-Free Question Decomposition](https://aclanthology.org/2025.starsem-1.20/) (Roy Dipta & Ferraro, *SEM 2025)
ACL