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.

翻译：同声传译（SiMT）要求在阅读源句的同时生成译文，因此需要一种策略来确定读取和生成词语的最佳时机。尽管大型语言模型（LLM）在各种自然语言处理任务中展现出卓越性能，但现有同声传译方法主要聚焦于传统Transformer模型，采用单一模型同时确定翻译策略并生成译文。然而，鉴于SiMT任务的复杂性，单个模型难以有效兼顾这两项任务。因此，有必要将SiMT任务解耦为策略决策与翻译两个子任务。我们提出SiLLM方法，将这两个子任务分别交由不同智能体执行，从而将LLM融入SiMT流程。策略决策智能体由传统SiMT模型管理，负责确定翻译策略；翻译智能体则利用LLM的能力，基于部分源句生成译文。两个智能体协同完成SiMT任务。为便于将传统SiMT模型确定的词级策略应用于LLM，我们提出一种适应LLM的词语级别策略。在两个数据集上的实验表明，仅需少量数据对LLM进行微调，SiLLM即可达到当前最优性能。