  Large language model (LLM) has achieved promising performance in multilingual
machine translation tasks through zero/few-shot prompts or prompt-tuning.
However, due to the mixture of multilingual data during the pre-training of
LLM, the LLM-based translation models face the off-target issue in both
prompt-based methods, including a series of phenomena, namely instruction
misunderstanding, translation with wrong language and over-generation. For this
issue, this paper introduces an
\textbf{\underline{A}}uto-\textbf{\underline{C}}onstriction
\textbf{\underline{T}}urning mechanism for \textbf{\underline{M}}ultilingual
\textbf{\underline{N}}eural \textbf{\underline{M}}achine
\textbf{\underline{T}}ranslation (\model), which is a novel supervised
fine-tuning mechanism and orthogonal to the traditional prompt-based methods.
In this method, \model automatically constructs a constrained template in the
target side by adding trigger tokens ahead of the ground truth. Furthermore,
trigger tokens can be arranged and combined freely to represent different task
semantics, and they can be iteratively updated to maximize the label
likelihood. Experiments are performed on WMT test sets with multiple metrics,
and the experimental results demonstrate that \model achieves substantially
improved performance across multiple translation directions and reduce the
off-target phenomena in the translation.
