The advent of transformers has fueled progress in machine translation. More recently large language models (LLMs) have come to the spotlight thanks to their generality and strong performance in a wide range of language tasks, including translation. Here we show that open-source LLMs perform on par with or better than some state-of-the-art baselines in simultaneous machine translation (SiMT) tasks, zero-shot. We also demonstrate that injection of minimal background information, which is easy with an LLM, brings further performance gains, especially on challenging technical subject-matter. This highlights LLMs' potential for building next generation of massively multilingual, context-aware and terminologically accurate SiMT systems that require no resource-intensive training or fine-tuning.

翻译：Transformer架构的出现推动了机器翻译领域的进步。近年来，大型语言模型（LLMs）因其通用性及在包括翻译在内的广泛语言任务中的强大性能而备受关注。本文研究表明，开源大型语言模型在同步机器翻译任务中以零样本方式达到或超越了部分最先进基线模型的性能。我们还证明，通过注入少量背景信息（这在大型语言模型中易于实现）可带来进一步的性能提升，尤其在具有挑战性的技术主题内容上表现更为显著。这凸显了大型语言模型在构建下一代大规模多语言、上下文感知且术语准确的同步翻译系统方面的潜力，此类系统无需资源密集型的训练或微调过程。