用大模型模拟网络诈骗对话，帮用户练习识别骗局。
Promoting Online Safety by Simulating Unsafe Conversations with LLMs
- 双大模型对话模拟真实诈骗场景，还原骗子与受害者互动。
- 用户对受害方模型给出反馈，提升识别风险能力。
- 结合学习科学，通过虚拟实践增强网络安全意识。
生成式AI，包括大型语言模型（LLMs），正被用于加速、扩大在线不安全对话的规模和类型。由于能够生成有说服力且类人化的文本，大模型降低了不良行为者制造不安全对话的门槛。本文探索通过让使用者体验并应对基于大模型的诈骗对话来促进在线安全。我们基于先前研究中大模型成功模拟诈骗对话的成果，结合学习科学中的发现——对假设行为提供反馈有助于学习。具体而言，我们设计了两个大模型相互对话，模拟现实中骗子与目标用户之间的不安全交流。系统中，用户需对目标大模型的表现提出反馈，从而在真实感情境中训练识别网络诈骗的能力。
原文摘要 · Abstract (English)
Generative AI, including large language models (LLMs) have the potential -- and already are being used -- to increase the speed, scale, and types of unsafe conversations online. LLMs lower the barrier for entry for bad actors to create unsafe conversations in particular because of their ability to generate persuasive and human-like text. In our current work, we explore ways to promote online safety by teaching people about unsafe conversations that can occur online with and without LLMs. We build on prior work that shows that LLMs can successfully simulate scam conversations. We also leverage research in the learning sciences that shows that providing feedback on one's hypothetical actions can promote learning. In particular, we focus on simulating scam conversations using LLMs. Our work incorporates two LLMs that converse with each other to simulate realistic, unsafe conversations that people may encounter online between a scammer LLM and a target LLM but users of our system are asked provide feedback to the target LLM.
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