若可去预设：通过无预设问题分解稳健验证主张

摘要

先前的研究表明，生成问题中的预设可能引入未经证实的假设，从而导致声明验证中的不一致性。此外，提示敏感性仍然是大型语言模型（LLMs）面临的一个重大挑战，导致性能波动高达3-6%。尽管最近的进展已缩小了这一差距，但我们的研究表明，提示敏感性仍然是一个持续存在的问题。为解决这一问题，我们提出了一种结构化和稳健的声明验证框架，该框架通过无预设、分解的问题进行推理。在多个提示、数据集和LLMs上的广泛实验表明，即使是最先进的模型仍然容易受到提示变异和预设的影响。我们的方法持续缓解了这些问题，实现了高达2-5%的改进。

English

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.

若可去预设：通过无预设问题分解稳健验证主张

If We May De-Presuppose: Robustly Verifying Claims through Presupposition-Free Question Decomposition

摘要

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