The article introduces GPT-who, a novel text detector leveraging the Uniform Information Density (UID) principle to distinguish between texts generated by Large Language Models (LLMs) and humans. By employing psycholinguistically-aware features, GPT-who achieves superior performance across various benchmark datasets compared to existing detectors like GLTR, GPTZero, and OpenAI detector. The method is computationally efficient, interpretable, and capable of accurately attributing authorship even in cases where the text is indiscernible. The study also explores the distribution of UID scores among different LLMs and human-generated texts, highlighting distinct patterns that aid in authorship prediction. Overall, GPT-who presents a promising approach rooted in psycholinguistic theories for detecting machine-generated text effectively.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Saranya Venk... at arxiv.org 03-19-2024
https://arxiv.org/pdf/2310.06202.pdfDeeper Inquiries