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GPTZero: Robust Detection of LLM-Generated Texts

George Alexandru Adam
Alexander Cui
Edwin Thomas
Emily Napier
Nazar Shmatko
Jacob Schnell
Jacob Junqi Tian
Alekhya Dronavalli
Edward Tian
Dongwon Lee
Main:7 Pages
11 Figures
Bibliography:3 Pages
9 Tables
Appendix:5 Pages
Abstract

While historical considerations surrounding text authenticity revolved primarily around plagiarism, the advent of large language models (LLMs) has introduced a new challenge: distinguishing human-authored from AI-generated text. This shift raises significant concerns, including the undermining of skill evaluations, the mass-production of low-quality content, and the proliferation of misinformation. Addressing these issues, we introduce GPTZero a state-of-the-art industrial AI detection solution, offering reliable discernment between human and LLM-generated text. Our key contributions include: introducing a hierarchical, multi-task architecture enabling a flexible taxonomy of human and AI texts, demonstrating state-of-the-art accuracy on a variety of domains with granular predictions, and achieving superior robustness to adversarial attacks and paraphrasing via multi-tiered automated red teaming. GPTZero offers accurate and explainable detection, and educates users on its responsible use, ensuring fair and transparent assessment of text.

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