  This paper proposes a simple method for controllable text generation based on
weighting logits with a free-form classifier, namely CAIF sampling. Using an
arbitrary text classifier, we adjust a small part of a language model's logits
and guide text generation towards or away from classifier prediction. We
experimented with toxicity avoidance and sentiment control tasks and showed
that the proposed method significantly outperforms recent PPLM, GeDi, and
DExperts on PPL and task accuracy metrics based on the external classifier of
generated texts. In addition, compared to other approaches, it is easier to
implement and tune and has significantly fewer restrictions and requirements.
