  Automatically generating textual content with desired attributes is an
ambitious task that people have pursued long. Existing works have made a series
of progress in incorporating unimodal controls into language models (LMs),
whereas how to generate controllable sentences with multimodal signals and high
efficiency remains an open question. To tackle the puzzle, we propose a new
paradigm of zero-shot controllable text generation with multimodal signals
(\textsc{ZeroGen}). Specifically, \textsc{ZeroGen} leverages controls of text
and image successively from token-level to sentence-level and maps them into a
unified probability space at decoding, which customizes the LM outputs by
weighted addition without extra training. To achieve better inter-modal
trade-offs, we further introduce an effective dynamic weighting mechanism to
regulate all control weights. Moreover, we conduct substantial experiments to
probe the relationship of being in-depth or in-width between signals from
distinct modalities. Encouraging empirical results on three downstream tasks
show that \textsc{ZeroGen} not only outperforms its counterparts on captioning
tasks by a large margin but also shows great potential in multimodal news
generation with a higher degree of control. Our code will be released at
https://github.com/ImKeTT/ZeroGen.
