LLMs Are Zero-Shot Context-Aware Simultaneous Translators
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929398431612928 |
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| author | Koshkin, Roman Sudoh, Katsuhito Nakamura, Satoshi |
| author_facet | Koshkin, Roman Sudoh, Katsuhito Nakamura, Satoshi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13476 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLMs Are Zero-Shot Context-Aware Simultaneous Translators Koshkin, Roman Sudoh, Katsuhito Nakamura, Satoshi Computation and Language 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. |
| title | LLMs Are Zero-Shot Context-Aware Simultaneous Translators |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.13476 |