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 machine learning model that is trained to predict the next word given an input context. As such, a model can generate text by generating one word at a time. These predictions can even, to some extent, be constrained by human-provided input to control what the model writes about. Due to their modeling power, large language models have the potential to generate textual output that is indistinguishable from human-written text to a non-expert reader. Language models achieve this with incredibly accura...

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
@sebgehr @ThomasScialom @NeuralGen @tatsu_hashimoto Yep, I'll be there to present https://t.co/xPdtmcilOm as a demo (and for Blackboxnlp)
@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