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The recent improvements of language models have drawn much attention to potential cases of use and abuse of automatically generated text. Great effort is put into the development of methods to detect machine generations among human-written text in order to avoid scenarios in which the large-scale generation of text with minimal cost and effort undermines the trust in human interaction and factual information online. While most of the current approaches rely on the availability of expensivedoi:10.7717/peerj-cs.443 pmid:33954234 pmcid:PMC8049133 fatcat:xtmb2i7e4rekbe4z4phaxqu7pi