说谎还是不说谎？研究LLMs在全球谎言传播中的偏见
To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs
AI总结：
研究LLMs在多语言多地区传播虚假信息的行为，揭示低资源语言和低HDI国家中谎言传播的系统性差异，提出GlobalLies数据集以支持减少全球虚假信息传播的策略。
摘要：
虚假信息正在上升，LLMs强大的写作能力降低了恶意行为者生成和传播虚假信息的门槛。我们研究了当提示LLMs传播虚假信息时，跨语言和目标国家的行为，并引入了GlobalLies，一个包含440个虚假信息生成提示模板和6,867个实体的多语言平行数据集，覆盖8种语言和195个国家。通过人类注释和大规模LLM-as-judge评估，我们发现虚假信息生成在讨论的国家上系统性地变化。LLMs传播谎言在许多低资源语言和HDI较低的国家中显著更高。我们发现现有缓解策略提供不均等保护：输入安全分类器存在跨语言差距，检索增强的事实核查在地区间因信息可用性不均而不一致。我们发布GlobalLies用于研究目的，旨在支持减少全球虚假信息传播的策略：https://github.com/zohaib-khan5040/globallies
英文摘要：
Misinformation is on the rise, and the strong writing capabilities of LLMs lower the barrier for malicious actors to produce and disseminate false information. We study how LLMs behave when prompted to spread misinformation across languages and target countries, and introduce GlobalLies, a multilingual parallel dataset of 440 misinformation generation prompt templates and 6,867 entities, spanning 8 languages and 195 countries. Using both human annotations and large-scale LLM-as-a-judge evaluations across hundreds of thousands of generations from state-of-the-art models, we show that misinformation generation varies systematically based on the country being discussed. Propagation of lies by LLMs is substantially higher in many lower-resource languages and for countries with a lower Human Development Index (HDI). We find that existing mitigation strategies provide uneven protection: input safety classifiers exhibit cross-lingual gaps, and retrieval-augmented fact-checking remains inconsistent across regions due to unequal information availability. We release GlobalLies for research purposes, aiming to support the development of mitigation strategies to reduce the spread of global misinformation: https://github.com/zohaib-khan5040/globallies
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
