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Energy-based models (EBMs), a.k.a. un-normalized models, have had recentsuccesses in continuous spaces. However, they have not been successfullyapplied to model text sequences. While decreasing the energy at trainingsamples is straightforward, mining (negative) samples where the energy shouldbe increased is difficult. In part, this is because standard gradient-basedmethods are not readily applicable when the input is high-dimensional anddiscrete. Here, we side-step this issue by generating negatives usingpre-trained auto-regressive language models. The EBM then works in the residualof the language model; and is trained to discriminate real text from textgenerated by the auto-regressive models. We investigate the generalizationability of residual EBMs, a pre-requisite for using them in other applications.We extensively analyze generalization for the task of classifying whether aninput is machine or human generated, a natural task given the training loss andhow we mine negatives. Overall, we observe that EBMs can generalize remarkablywell to changes in the architecture of the generators producing negatives.However, EBMs exhibit more sensitivity to the training set used by suchgenerators.

Real or Fake? Learning to Discriminate Machine from Human Generated Text