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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.13042 |
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| _version_ | 1866911446577709056 |
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| author | Adam, George Alexandru Cui, Alexander Thomas, Edwin Napier, Emily Shmatko, Nazar Schnell, Jacob Tian, Jacob Junqi Dronavalli, Alekhya Tian, Edward Lee, Dongwon |
| author_facet | Adam, George Alexandru Cui, Alexander Thomas, Edwin Napier, Emily Shmatko, Nazar Schnell, Jacob Tian, Jacob Junqi Dronavalli, Alekhya Tian, Edward Lee, Dongwon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13042 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GPTZero: Robust Detection of LLM-Generated Texts Adam, George Alexandru Cui, Alexander Thomas, Edwin Napier, Emily Shmatko, Nazar Schnell, Jacob Tian, Jacob Junqi Dronavalli, Alekhya Tian, Edward Lee, Dongwon Machine Learning 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. |
| title | GPTZero: Robust Detection of LLM-Generated Texts |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.13042 |