liamdugan/human-detection
Code for the AAAI 2023 Paper "Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Text"
This project provides a comprehensive dataset and analysis tools for understanding how well humans can distinguish between text written by humans and text generated by AI. It takes in human judgments on various text passages and outputs insights into detection accuracy and the types of errors AI models make across different writing styles. Researchers, educators, and content strategists interested in the implications of generative AI would find this useful.
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Use this if you are a researcher or educator studying human perception of AI-generated content and need a robust dataset and analytical framework.
Not ideal if you are looking for a tool to automatically detect AI-generated text or to develop new generative AI models.
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17
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Language
Jupyter Notebook
License
MIT
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Last pushed
Oct 29, 2024
Commits (30d)
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