Where Am I From? Identifying Origin of LLM-generated Content

Generative models, particularly large language models (LLMs), have achieved remarkable success in producing natural and high-quality content.However, their widespread adoption raises concerns regarding copyright infringement, privacy violations, and security risks associated with AI-generated content.To address these concerns, we propose a novel digital forensics framework for LLMs, enabling the tracing of AI-generated content back to its source.This framework embeds a secret watermark directly into the generated output, eliminating the need for model retraining.To enhance traceability, especially for short outputs, we introduce a "depth watermark" that strengthens the link between content and generator.Our approach ensures accurate tracing while maintaining the quality of the generated content.Extensive experiments across various settings and datasets validate the effectiveness and robustness of our proposed framework.Language Model Corning officials expect to produce functioning optically switched interconnects later this year and will conduct further research in 2012 while the prototypes are under development.Meanwhile, IBM plans to spinoff the research effort in 2012 as its Opheliant division, Generator Generator's Code Query Embedded Generate