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With the recent proliferation of Large Language Models (LLMs), there has beenan increasing demand for tools to detect machine-generated text. The effectivedetection of machine-generated text face two pertinent problems: First, theyare severely limited in generalizing against real-world scenarios, wheremachine-generated text is produced by a variety of generators, including butnot limited to GPT-4 and Dolly, and spans diverse domains, ranging fromacademic manuscripts to social media posts. Second, existing detectionmethodologies treat texts produced by LLMs through a restrictive binaryclassification lens, neglecting the nuanced diversity of artifacts generated bydifferent LLMs. In this work, we undertake a systematic study on the detectionof machine-generated text in real-world scenarios. We first study theeffectiveness of state-of-the-art approaches and find that they are severelylimited against text produced by diverse generators and domains in the realworld. Furthermore, t-SNE visualizations of the embeddings from a pretrainedLLM's encoder show that they cannot reliably distinguish between human andmachine-generated text. Based on our findings, we introduce a novel system,T5LLMCipher, for detecting machine-generated text using a pretrained T5 encodercombined with LLM embedding sub-clustering to address the text produced bydiverse generators and domains in the real world. We evaluate our approachacross 9 machine-generated text systems and 9 domains and find that ourapproach provides state-of-the-art generalization ability, with an averageincrease in F1 score on machine-generated text of 19.6\% on unseen generatorsand domains compared to the top performing existing approaches and correctlyattributes the generator of text with an accuracy of 93.6\%.

Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text