GeDi: Generative Discriminator Guided Sequence Generation

While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of the distribution they generate. This is especially problematic because datasets used for training large LMs usually contain significant toxicity, hate, bias, and negativity. One promising approach to address this is to use discriminators to guide decoding from LMs, but existing methods for this are too slow to be useful in practice for many applications. We present GeDi as a significantly more efficient discriminator-based approach for guiding decoding. 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 controllability on par with or better than previous controllable generation methods. GeDi results in significantly faster generation speeds than the only previous method that achieved comparable controllability in our experiments. We also show that GeDi can make GPT-2 and GPT-3 significantly less toxic while maintaining linguistic fluency, without sacrificing significantly on generation speed. Lastly, we find training GeDi on only three topics allows us to controllably generate new topics zero-shot from just a keyword.