  Imposing constraints on machine translation systems presents a challenging
issue because these systems are not trained to make use of constraints in
generating adequate, fluent translations. In this paper, we leverage the
capabilities of large language models (LLMs) for constrained translation, given
that LLMs can easily adapt to this task by taking translation instructions and
constraints as prompts. However, LLMs cannot always guarantee the adequacy of
translation, and, in some cases, ignore the given constraints. This is in part
because LLMs might be overly confident in their predictions, overriding the
influence of the constraints. To overcome this overiding behaviour, we propose
to add a revision process that encourages LLMs to correct the outputs by
prompting them about the constraints that have not yet been met. We evaluate
our approach on four constrained translation tasks, encompassing both lexical
and structural constraints in multiple constraint domains. Experiments show
15\% improvement in constraint-based translation accuracy over standard LLMs
and the approach also significantly outperforms neural machine translation
(NMT) state-of-the-art methods.
