Position: It's Time to Act on the Risk of Efficient Personalized Text Generation

TL;DR

The paper highlights the urgent safety risks posed by accessible personalized AI text models capable of impersonating individuals, emphasizing the need for proactive measures to mitigate misuse such as fraud and impersonation.

Contribution

It introduces the safety concerns associated with personalized text generation models and calls for the research community to address these emerging risks proactively.

Findings

Personalized models can be trained cheaply on consumer hardware.

Such models enable impersonation for malicious purposes.

Current research overlooks these safety risks.

Abstract

The recent surge in high-quality open-source Generative AI text models (colloquially: LLMs), as well as efficient finetuning techniques, have opened the possibility of creating high-quality personalized models that generate text attuned to a specific individual's needs and are capable of credibly imitating their writing style by refining an open-source model with that person's own data. The technology to create such models is accessible to private individuals, and training and running such models can be done cheaply on consumer-grade hardware. While these advancements are a huge gain for usability and privacy, this position paper argues that the practical feasibility of impersonating specific individuals also introduces novel safety risks. For instance, this technology enables the creation of phishing emails or fraudulent social media accounts, based on small amounts of publicly available text, or by the individuals themselves to escape AI text detection. We further argue that these risks are complementary to - and distinct from - the much-discussed risks of other impersonation attacks such as image, voice, or video deepfakes, and are not adequately addressed by the larger research community, or the current generation of open- and closed-source models.