标题： LLM清洗后的虚假新闻检测：测量与解释 显示英文标题

标题： Fake News Detection After LLM Laundering: Measurement and Explanation

摘要： 凭借其先进能力，大型语言模型（LLMs）可以生成高度可信且与上下文相关的虚假新闻，这可能导致错误信息的传播。 尽管已有大量关于人类撰写文本的虚假新闻检测研究，但检测LLM生成的虚假新闻领域仍研究不足。 本研究评估了检测器在识别LLM改写虚假新闻方面的效果，特别是确定在检测流程中添加改写步骤是否有助于或阻碍检测。 本研究的贡献包括：(1) 检测器在检测LLM改写的虚假新闻方面比检测人类撰写的文本更加困难，(2) 我们发现哪些模型在哪些任务上表现优异（逃避检测、为逃避检测而改写以及为语义相似性而改写）。 (3) 通过LIME解释，我们发现了一个可能导致检测失败的原因：情感变化。 (4) 我们发现了一个令人担忧的改写质量测量趋势：样本在BERTSCORE较高的情况下仍表现出情感变化。 (5) 我们提供了一对数据集，在现有数据集中增加了改写输出和评分。 该数据集可在GitHub上获取 显示英文摘要

摘要： With their advanced capabilities, Large Language Models (LLMs) can generate highly convincing and contextually relevant fake news, which can contribute to disseminating misinformation. Though there is much research on fake news detection for human-written text, the field of detecting LLM-generated fake news is still under-explored. This research measures the efficacy of detectors in identifying LLM-paraphrased fake news, in particular, determining whether adding a paraphrase step in the detection pipeline helps or impedes detection. This study contributes: (1) Detectors struggle to detect LLM-paraphrased fake news more than human-written text, (2) We find which models excel at which tasks (evading detection, paraphrasing to evade detection, and paraphrasing for semantic similarity). (3) Via LIME explanations, we discovered a possible reason for detection failures: sentiment shift. (4) We discover a worrisome trend for paraphrase quality measurement: samples that exhibit sentiment shift despite a high BERTSCORE. (5) We provide a pair of datasets augmenting existing datasets with paraphrase outputs and scores. The dataset is available on GitHub