If We May De-Presuppose: Robustly Verifying Claims through Presupposition-Free Question Decomposition
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912611165011968 |
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| author | Dipta, Shubhashis Roy Ferraro, Francis |
| author_facet | Dipta, Shubhashis Roy Ferraro, Francis |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_16838 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | If We May De-Presuppose: Robustly Verifying Claims through Presupposition-Free Question Decomposition Dipta, Shubhashis Roy Ferraro, Francis Computation and Language 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. |
| title | If We May De-Presuppose: Robustly Verifying Claims through Presupposition-Free Question Decomposition |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.16838 |