If We May De-Presuppose: Robustly Verifying Claims through
  Presupposition-Free Question Decomposition
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.
68adeb86364411bea07df5ce
Shubhashis Roy Dipta
claimed_verified
2025-08-26T19:05:27.865Z
638940232a897944ea5d2a41
https://cdn-avatars.huggingface.co/v1/production/uploads/638940232a897944ea5d2a41/G3tmDlB_vagrKj1KjsQP6.jpeg
Shubhashis Roy Dipta
user
dipta007
68adeb86364411bea07df5cf
Francis Ferraro
If We May De-Presuppose: Robustly Verifying Claims through
  Presupposition-Free Question Decomposition
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.
A structured claim verification framework reduces prompt sensitivity and presupposition issues in large language models, improving performance by 2-5%.
presupposition claim verification prompt sensitivity large language models LLMs decomposed questions