Robust Detection of Machine-Generated Text Across Diverse Generators and Domains

Existing methods for detecting machine-generated text face severe limitations in generalizing to diverse generators and domains in real-world scenarios. This work introduces T5LLMCipher, a novel system that leverages the embeddings from LLM encoders to robustly detect and attribute machine-generated text, outperforming state-of-the-art approaches.