LLMs Are Zero-Shot Context-Aware Simultaneous Translators

• URL: http://arxiv.org/abs/2406.13476v3
• Date: Tue, 25 Jun 2024 04:45:19 GMT
• Title: LLMs Are Zero-Shot Context-Aware Simultaneous Translators
• Authors: Roman Koshkin, Katsuhito Sudoh, Satoshi Nakamura,
• Abstract summary: Large language models (LLMs) have come to the spotlight thanks to their generality and strong performance in a wide range of language tasks. 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.
• Score: 16.260150631363313
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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