Misinformation poses a variety of risks, such as undermining public trust and
distorting factual discourse. Large Language Models (LLMs) like GPT-4 have been
shown effective in mitigating misinformation, particularly in handling
statements where enough context is provided. However, they struggle to assess
ambiguous or context-deficient statements accurately. This work introduces a
new method to resolve uncertainty in such statements. We propose a framework to
categorize missing information and publish category labels for the LIAR-New
dataset, which is adaptable to cross-domain content with missing information.
We then leverage this framework to generate effective user queries for missing
context. Compared to baselines, our method improves the rate at which generated
questions are answerable by the user by 38 percentage points and classification
performance by over 10 percentage points macro F1. Thus, this approach may
provide a valuable component for future misinformation mitigation pipelines.

使用新的方法解决 GPT-4 等大型语言模型在处理模糊或缺乏背景信息的陈述时的不确定性，并通过基于 LIAR-New 数据集的分类标签提出一个可适用于跨领域内容的框架来解析丢失的信息。我们利用这个框架生成有效的用户查询，相比基准方法，我们的方法提高了用户可回答的问题的比例 38 个百分点，宏观 F1 分类性能提高了 10 个百分点以上。因此，该方法可能成为未来对抗虚假信息的有价值组成部分。