构建具有韧性的信息生态系统：大规模的大语言模型生成的说服攻击数据集
Building Resilient Information Ecosystems: Large LLM-Generated Dataset of Persuasion Attacks
AI总结：
本文提出了一种大规模的大语言模型生成的说服攻击数据集，用于研究和防御机构沟通中的说服策略，以提升信息生态系统的韧性。
摘要：
组织的沟通对于公众信任至关重要，但生成式AI模型的兴起带来了重大挑战，因为这些模型能够以高速度和规模生成具有说服力的内容，形成与政府和商业组织官方信息相竞争的叙事。这使机构处于被动位置，往往不清楚这些模型如何构建其说服策略，使维持沟通有效性变得更加困难。在本文中，我们介绍了一个大规模的大语言模型生成的说服攻击数据集，其中包括由GPT-4、Gemma 2和Llama 3.1生成的134,136个攻击，这些攻击针对机构新闻。这些攻击涵盖了SemEval 2023任务3中的23种说服技术，针对来自十个机构的972份新闻稿。生成的攻击分为两种媒介，即新闻稿声明和社会媒体帖子，涵盖了长篇和短篇的沟通策略。我们分析了这些说服攻击的道德共鸣，以了解其攻击向量。GPT-4的攻击主要集中在关怀上，同时涉及权威和忠诚。Gemma 2强调关怀和权威，而Llama 3.1则以忠诚和关怀为中心。分析不同模型生成的说服攻击将有助于主动防御，使组织能够创建声誉装甲，并推动信息生态系统中有效且具有韧性的沟通发展。
英文摘要：
Organization's communication is essential for public trust, but the rise of generative AI models has introduced significant challenges by generating persuasive content that can form competing narratives with official messages from government and commercial organizations at speed and scale. This has left agencies in a reactive position, often unaware of how these models construct their persuasive strategies, making it more difficult to sustain communication effectiveness. In this paper, we introduce a large LLM-generated persuasion attack dataset, which includes 134,136 attacks generated by GPT-4, Gemma 2, and Llama 3.1 on agency news. These attacks span 23 persuasive techniques from SemEval 2023 Task 3, directed toward 972 press releases from ten agencies. The generated attacks come in two mediums, press release statements and social media posts, covering both long-form and short-form communication strategies. We analyzed the moral resonance of these persuasion attacks to understand their attack vectors. GPT-4's attacks mainly focus on Care, with Authority and Loyalty also playing a role. Gemma 2 emphasizes Care and Authority, while Llama 3.1 centers on Loyalty and Care. Analyzing LLM-generated persuasive attacks across models will enable proactive defense, allow to create the reputation armor for organizations, and propel the development of both effective and resilient communications in the information ecosystem.
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。
