Real-world simultaneous machine translation (SimulMT) systems face more challenges than just the quality-latency trade-off. They also need to address issues related to robustness with noisy input, processing long contexts, and flexibility for knowledge injection. These challenges demand models with strong language understanding and generation capabilities which may not often equipped by dedicated MT models. In this paper, we investigate the possibility of applying Large Language Models (LLM) to SimulMT tasks by using existing incremental-decoding methods with a newly proposed RALCP algorithm for latency reduction. We conducted experiments using the \texttt{Llama2-7b-chat} model on nine different languages from the MUST-C dataset. The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics. Further analysis indicates that LLM has advantages in terms of tuning efficiency and robustness. However, it is important to note that the computational cost of LLM remains a significant obstacle to its application in SimulMT.\footnote{We will release our code, weights, and data with publication.}

翻译：现实场景中的同声传译（SimulMT）系统面临超越质量-延迟权衡的更多挑战，还需应对含噪输入的鲁棒性、长上下文处理及知识注入灵活性等问题。这些挑战要求模型具备强大的语言理解与生成能力，而专用机器翻译模型往往不具备此类能力。本文通过采用现有增量解码方法，结合新提出的用于延迟缩减的RALCP算法，探究将大语言模型（LLM）应用于同声传译任务的可能性。我们使用MUST-C数据集中涉及九种不同语言的\texttt{Llama2-7b-chat}模型进行实验，结果表明：在BLEU与LAAL指标上，LLM性能优于专用机器翻译模型。进一步分析表明，LLM在调优效率与鲁棒性方面具有优势，但需注意其计算成本仍是应用于同声传译领域的主要障碍。\footnote{论文发表后，我们将公开代码、模型权重及数据。}