SiLLM: Large Language Models for Simultaneous Machine Translation
Shoutao Guo Shaolei Zhang Zhengrui Ma Min Zhang Yang Feng
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.