As Large Language Models (LLMs) become more proficient, their misuse in
large-scale viral disinformation campaigns is a growing concern. This study
explores the capability of ChatGPT to generate unconditioned claims about the
war in Ukraine, an event beyond its knowledge cutoff, and evaluates whether
such claims can be differentiated by human readers and automated tools from
human-written ones. We compare war-related claims from ClaimReview, authored by
IFCN-registered fact-checkers, and similar short-form content generated by
ChatGPT. We demonstrate that ChatGPT can produce realistic, target-specific
disinformation cheaply, fast, and at scale, and that these claims cannot be
reliably distinguished by humans or existing automated tools.

ChatGPT 在乌克兰战争等其知识范围之外的事件中生成的消息可以与由人类撰写的消息以及 ClaimReview 中的关于战争的声明无法可靠地区分开来。

遮蔽哄骗：规避 ChatGPT 的安全措施，大规模生成难以检测的虚假宣称

Lying Blindly: Bypassing ChatGPT's Safeguards to Generate Hard-to-Detect  Disinformation Claims at Scale

Value chain data is crucial to navigate economic disruptions, such as those
caused by the COVID-19 pandemic and the war in Ukraine. Yet, despite its
importance, publicly available value chain datasets, such as the ``World
Input-Output Database'', ``Inter-Country Input-Output Tables'', ``EXIOBASE'' or
the ``EORA'', lack detailed information about products (e.g. Radio Receivers,
Telephones, Electrical Capacitors, LCDs, etc.) and rely instead on more
aggregate industrial sectors (e.g. Electrical Equipment, Telecommunications).
Here, we introduce a method based on machine learning and trade theory to infer
product-level value chain relationships from fine-grained international trade
data. We apply our method to data summarizing the exports and imports of 300+
world regions (e.g. states in the U.S., prefectures in Japan, etc.) and 1200+
products to infer value chain information implicit in their trade patterns.
Furthermore, we use proportional allocation to assign the trade flow between
regions and countries. This work provides an approximate method to map value
chain data at the product level with a relevant trade flow, that should be of
interest to people working in logistics, trade, and sustainable development.

利用机器学习和贸易理论，从细粒度国际贸易数据中推断产品级价值链关系，为物流、贸易和可持续发展领域的人员提供了一个与相关贸易流动相关的产品级价值链数据的近似映射方法。