Papers with GPTZero
GPT-who: An Information Density-based Machine-Generated Text Detector (2024.findings-naacl)
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| Challenge: | Large Language Models (LLMs) generate misinformation, memorized content, plagiarized content, toxic speech, and hallucinated content. |
| Approach: | They propose a statistical detector that uses UID to model the unique statistical signature of each LLM and human author for accurate detection. |
| Outcome: | The proposed method outperforms state-of-the-art detectors by over 20% across domains. |
Ghostbuster: Detecting Text Ghostwritten by Large Language Models (2024.naacl-long)
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| Challenge: | Ghostbuster is a system that passes documents through weaker language models, runs a structured search over possible combinations of their features, and trains a classifier on the selected features. |
| Approach: | They propose a method that passes documents through weaker language models, runs a structured search over possible combinations of their features, and trains a classifier on the selected features. |
| Outcome: | The proposed method outperforms existing detectors and a new baseline on student essays, creative writing, and news articles. |