Before revision:  To overcome these limitations, we propose generative discriminator (GeDi ) guided contrastive generation, which uses CC-LMs as generative discriminators (GeDis) to efficiently guide generation from a (potentially much larger) LM towards a desired attribute. In our human evaluation experiments, we show that GeDis trained for sentiment control on movie reviews are able to control the tone of book text.
After revision:  To overcome these limitations, we propose generative discriminator (GeDi ) guided contrastive generation, which uses CC-LMs as generative discriminators (GeDis) to efficiently guide generation from large LMs to make them safer and more controllable. GeDi guides generation at each step by computing classification probabilities for all possible next tokens via Bayes rule by normalizing over two class-conditional distributions; one conditioned on the desired attribute, or control code, and another conditioned on the undesired attribute, or anti control code. We find that GeDi gives stronger controllability than the state of the art method while also achieving generation speeds more than 30 times faster. Additionally, training GeDi on movie reviews are able to control the tone of book text.