An Information-Theoretic Method for Detecting Edits in AI-Generated Text

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Bibliographic Details
Main Authors: Idan Kashtan, Alon Kipnis
Format: Article
Language:English
Published: The MIT Press 2024-08-01
Series:Harvard Data Science Review
Online Access:http://dx.doi.org/10.1162/99608f92.5dbf3265
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author Idan Kashtan
Alon Kipnis
author_facet Idan Kashtan
Alon Kipnis
author_sort Idan Kashtan
collection DOAJ
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institution Kabale University
issn 2644-2353
language English
publishDate 2024-08-01
publisher The MIT Press
record_format Article
series Harvard Data Science Review
spelling doaj-art-dcd4d2341f1e41cc962219589d6603b82024-12-09T20:13:45ZengThe MIT PressHarvard Data Science Review2644-23532024-08-01Special Issue 510.1162/99608f92.5dbf3265An Information-Theoretic Method for Detecting Edits in AI-Generated TextIdan KashtanAlon Kipnishttp://dx.doi.org/10.1162/99608f92.5dbf3265
spellingShingle Idan Kashtan
Alon Kipnis
An Information-Theoretic Method for Detecting Edits in AI-Generated Text
Harvard Data Science Review
title An Information-Theoretic Method for Detecting Edits in AI-Generated Text
title_full An Information-Theoretic Method for Detecting Edits in AI-Generated Text
title_fullStr An Information-Theoretic Method for Detecting Edits in AI-Generated Text
title_full_unstemmed An Information-Theoretic Method for Detecting Edits in AI-Generated Text
title_short An Information-Theoretic Method for Detecting Edits in AI-Generated Text
title_sort information theoretic method for detecting edits in ai generated text
url http://dx.doi.org/10.1162/99608f92.5dbf3265
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