Despite speculation that recent large language models (LLMs) are likely to be
used maliciously to improve the quality or scale of influence operations,
uncertainty persists regarding the economic value that LLMs offer
propagandists. This research constructs a model of costs facing propagandists
for content generation at scale and analyzes (1) the potential savings that
LLMs could offer propagandists, (2) the potential deterrent effect of
monitoring controls on API-accessible LLMs, and (3) the optimal strategy for
propagandists choosing between multiple private and/or open source LLMs when
conducting influence operations. Primary results suggest that LLMs need only
produce usable outputs with relatively low reliability (roughly 25%) to offer
cost savings to propagandists, that the potential reduction in content
generation costs can be quite high (up to 70% for a highly reliable model), and
that monitoring capabilities have sharply limited cost imposition effects when
alternative open source models are available. In addition, these results
suggest that nation-states -- even those conducting many large-scale influence
operations per year -- are unlikely to benefit economically from training
custom LLMs specifically for use in influence operations.

大型语言模型对宣传者在内容生成成本方面提供可观的节省，并在影响操作中具有潜在的减少成本的能力。

生成语言模型和影响操作的成本分析

A Cost Analysis of Generative Language Models and Influence Operations

Influence operations are large-scale efforts to manipulate public opinion.
The rapid detection and disruption of these operations is critical for healthy
public discourse. Emergent AI technologies may enable novel operations which
evade current detection methods and influence public discourse on social media
with greater scale, reach, and specificity. New methods with inductive learning
capacity will be needed to identify these novel operations before they
indelibly alter public opinion and events. We develop an inductive learning
framework which: 1) determines content- and graph-based indicators that are not
specific to any operation; 2) uses graph learning to encode abstract signatures
of coordinated manipulation; and 3) evaluates generalization capacity by
training and testing models across operations originating from Russia, China,
and Iran. We find that this framework enables strong cross-operation
generalization while also revealing salient
indicators$\unicode{x2013}$illustrating a generic approach which directly
complements transductive methodologies, thereby enhancing detection coverage.

本文提出了一种基于归纳学习的方法识别操纵操作，并借助该方法检测来自俄罗斯、中国和伊朗的操作，在跨操作普适性和检测范围方面取得了良好的效果。