LLMs Are Zero-Shot Context-Aware Simultaneous Translators
<|reference_start|>LLMs Are Zero-Shot Context-Aware Simultaneous Translators: 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.<|reference_end|>
@article{koshkin2024llms,
    title={LLMs Are Zero-Shot Context-Aware Simultaneous Translators},
    author={Roman Koshkin, Katsuhito Sudoh, Satoshi Nakamura},
    journal={arXiv preprint arXiv:2406.13476},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13476},
    primaryClass={cs.CL}
}