MixCE: Training Autoregressive Language Models by Mixing Forward and
  Reverse Cross-Entropies
Authors: name: Shiyue Zhang; name: Shijie Wu; name: Ozan Irsoy; name: Steven Lu; name: Mohit Bansal; name: Mark Dredze; name: David Rosenberg
Published: 2023-05-26 14:14:51

Abstract: Autoregressive language models are trained by minimizing the cross-entropy of
the model distribution Q relative to the data distribution P -- that is,
minimizing the forward cross-entropy, which is equivalent to maximum likelihood
estimation (MLE). We have observed that models trained in this way may
"over-generalize", in the sense that they produce non-human-like text.
Moreover, we believe that reverse cross-entropy, i.e., the cross-entropy of P
relative to Q, is a better reflection of how a human would evaluate text
generated by a model. Hence, we propose learning with MixCE, an objective that
mixes the forward and reverse cross-entropies. We evaluate models trained with
this objective on synthetic data settings (where P is known) and real data, and
show that the resulting models yield better generated text without complex
decoding strategies. Our code and models are publicly available at
https://github.com/bloomberg/mixce-acl2023