  There has been considerable progress made towards conversational models that
generate coherent and fluent responses; however, this often involves training
large language models on large dialogue datasets, such as Reddit. These large
conversational models provide little control over the generated responses, and
this control is further limited in the absence of annotated conversational
datasets for attribute specific generation that can be used for fine-tuning the
model. In this paper, we first propose and evaluate plug-and-play methods for
controllable response generation, which does not require dialogue specific
datasets and does not rely on fine-tuning a large model. While effective, the
decoding procedure induces considerable computational overhead, rendering the
conversational model unsuitable for interactive usage. To overcome this, we
introduce an approach that does not require further computation at decoding
time, while also does not require any fine-tuning of a large language model. We
demonstrate, through extensive automatic and human evaluation, a high degree of
control over the generated conversational responses with regard to multiple
desired attributes, while being fluent.
