揭开AI的阴影：调查大型语言模型中的欺骗能力
Unmasking the Shadows of AI: Investigating Deceptive Capabilities in Large Language Models
AI总结：
本研究批判性调查大型语言模型中的欺骗行为，分类四种欺骗类型并评估其社会影响，提出治理与教育应对策略。
摘要：
这项研究批判性地探索了AI欺骗的复杂领域，重点关注大型语言模型（LLMs）的欺骗行为。我的目标是阐明这一问题，审视围绕它的讨论，并进而深入探讨其分类和影响。本文首先评估了2023年AI安全峰会（ASS）并介绍了LLMs，强调了构成其欺骗行为基础的多维偏见。文献综述涵盖了四种欺骗类型：策略性欺骗、模仿、谄媚和不忠实的推理，以及它们所带来的社会影响和风险。最后，我对应对欺骗性AI持续挑战的各个方面采取了评价性的立场。这包括对国际协作治理的考虑、个人与AI互动的重新配置、实际调整的提议以及数字教育的特定要素。
英文摘要：
This research critically navigates the intricate landscape of AI deception, concentrating on deceptive behaviours of Large Language Models (LLMs). My objective is to elucidate this issue, examine the discourse surrounding it, and subsequently delve into its categorization and ramifications. The essay initiates with an evaluation of the AI Safety Summit 2023 (ASS) and introduction of LLMs, emphasising multidimensional biases that underlie their deceptive behaviours.The literature review covers four types of deception categorised: Strategic deception, Imitation, Sycophancy, and Unfaithful Reasoning, along with the social implications and risks they entail. Lastly, I take an evaluative stance on various aspects related to navigating the persistent challenges of the deceptive AI. This encompasses considerations of international collaborative governance, the reconfigured engagement of individuals with AI, proposal of practical adjustments, and specific elements of digital education.
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。
