Real or Fake? Learning to Discriminate Machine from Human Generated Text
Table 2: “In domain” generalization accuracy of EBMs (each row) on various text corpora. A column corresponds to the corpus used to get positives and to fit the train and test language models, which are TransfBig (§4.2) with different initial seeds. The last row is the accuracy when using as energy the log-probability of the training language model over the whole sequence.
['[EMPTY]', 'Books', 'CCNews', 'Wiki']
The Wikipedia dataset has lower accuracy because the EBM overfits to this smaller dataset. Interestingly, our energy function was trained using a fixed length input with a prefix.