  Large language models (LLMs) have achieved state-of-the-art performance in
various language processing tasks, motivating their adoption in simultaneous
translation. Current fine-tuning methods to adapt LLMs for simultaneous
translation focus on prompting optimization strategies using either data
augmentation or prompt structure modifications. However, these methods suffer
from several issues, such as unnecessarily expanded training sets,
computational inefficiency from dumping the key and value cache, increased
prompt sizes, or restriction to a single decision policy. To eliminate these
issues, in this work, we propose SimulMask, a new paradigm for fine-tuning LLMs
for simultaneous translation. It utilizes a novel attention mask approach that
models simultaneous translation during fine-tuning by masking attention for a
desired decision policy. Applying the proposed SimulMask on a Falcon LLM for
the IWSLT 2017 dataset, we have observed a significant translation quality
improvement compared to state-of-the-art prompting optimization strategies on
five language pairs while reducing the computational cost.
