Catching Unicorns with GLTR

try our demo By Hendrik Strobelt and Sebastian Gehrmann -- reviewed by Alexander Rush A collaboration of MIT-IBM Watson AI lab and HarvardNLP We introduce GLTR to inspect the visual footprint of automatically generated tex. It enables a forensic analysis of how likely an automatic system generated a text. Check out the live DEMO In recent years, the natural language processing community has seen the development of increasingly larger and larger language models. A language model is a mach

4 mentions: @sebgehr@sebgehr@julien_c@sebgehr
Date: 2019/06/12 18:48

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@sebgehr Our ACL Demo paper for https://t.co/xPdtmc0KpM is finally on Arxiv! We not only found that models can defend against themselves, but that our interface can help humans spot generated text. Check it out here: https://t.co/ZYtDRYAkJZ With @MITIBMLab @hen_str @srush_nlp https://t.co/8egKnOb61T
@julien_c @ClementDelangue @sararahmcb @betaworksVC @Borthwick You can paste it into https://t.co/mZXyuaZZfH and see if machines are better than humans at detecting it
@sebgehr Shameless plug: this is exactly why our tool https://t.co/xPdtmcilOm works! The samples are rated highly by humans, and therefore we can exploit the underdiversity to detect generated text!

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