Simultaneous Machine Translation (SiMT) generates translations while reading
the source sentence, necessitating a policy to determine the optimal timing for
reading and generating words. Despite the remarkable performance achieved by
Large Language Models (LLM) across various NLP tasks, existing SiMT methods
predominantly focus on conventional transformers, employing a single model to
concurrently determine the policy and generate the translations. However, given
the complexity of SiMT, it is challenging to effectively address both tasks
with a single model. Therefore, there is a need to decouple the SiMT task into
policy-decision and translation sub-tasks. We propose SiLLM, which delegates
the two sub-tasks to separate agents, thereby incorporating LLM into SiMT. The
policy-decision agent is managed by a conventional SiMT model, responsible for
determining the translation policy. The translation agent, leveraging the
capabilities of LLM, generates translation using the partial source sentence.
The two agents collaborate to accomplish SiMT. To facilitate the application of
token-level policies determined by conventional SiMT models to LLM, we propose
a word-level policy adapted for LLM. Experiments on two datasets demonstrate
that, with a small amount of data for fine-tuning LLM, SiLLM attains
state-of-the-art performance.

同时机器翻译 (SiMT) 使用一个策略来确定最佳的阅读和生成单词的时机。我们提出了 SiLLM，将 SiMT 任务分解为策略决策和翻译子任务，并引入了大型语言模型 (LLM)。两个代理协作完成 SiMT，最终实现了最新技术水平的性能表现。