Real or Fake? Learning to Discriminate Machine from Human Generated Text
Figure 1: Discrimination accuracy as a function of the ratio between the prefix length and the total length of the sequence on the Wikitext dataset.
['[EMPTY]', 'Accuracy']
First, we investigate the dependency between performance of the energy functions and length of the prefix. We trained BiLSTMSmall and UniTransf models on examples with varying prefix length from the Wikitext corpus, and computed the accuracy for each prefix length independently. The unconditional case, i.e. zero prefix length, is the easiest, while prefixes of length 120 and 140 that are the main experimental setup in this work, are the hardest. Using more negatives and harder negatives improves accuracy.