File:A possible scenario of GPT-4 used for misinformation.png

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From the preprint "Sparks of Artificial General Intelligence: Early experiments with GPT-4"

Summary

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Description
English: "Like any powerful technology, LLMs can be used by malevolent actors to do damage. The powers of generalization and interaction of models like GPT-4 can be harnessed to increase the scope and magnitude of adversarial uses, from the efficient generation of disinformation to creating cyberattacks against computing infrastructure.

The interactive powers and models of human judgment and decision making can be employed to manipulate, persuade, or influence people in significant ways. GPT-4 and descendants can be harnessed to contextualize and personalize interactions to maximize the impact of their generations. While many of these adverse use cases are possible today with a motivated adversary creating content, new powers of efficiency and scale can be enabled with automation using the LLMs, including uses aimed at constructing disinformation plans that generate and compose multiple pieces of content for persuasion over short and long-time scales [Hor22].

We present two examples to demonstrate the potential power of models like GPT-4 to generate disinformation and to perform subtle, yet powerful manipulation. In the example displayed in Figure 9.1, we query the model to create a plan for disinformation. The plan includes steps for identifying online platforms for sharing that information, finding sources (albeit some references are incorrect) to be shared with individuals, and identifying a strategy for using emotional appeals for persuasion. Follow-up interactions with the model (See Figure 9.2) show how the model might be used to realize the attack by creating messages that are customized for triggering different emotional reactions. Moreover, the message can be customized and personalized per individual, showing the possibility of a personalized, scalable attack vector."
Date
Source https://arxiv.org/abs/2303.12712
Author Authors of the study: Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang (all at Microsoft Research)

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current14:11, 8 May 2023Thumbnail for version as of 14:11, 8 May 20231,308 × 1,603 (704 KB)Prototyperspective (talk | contribs)Uploaded a work by Authors of the study: Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang (all at Microsoft Research) from https://arxiv.org/abs/2303.12712 with UploadWizard

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