Computer Science > Computation and Language
arXiv:2406.13476 (cs)
[Submitted on 19 Jun 2024 (v1), last revised 25 Jun 2024 (this version, v3)]
Title:LLMs Are Zero-Shot Context-Aware Simultaneous Translators
View a PDF of the paper titled LLMs Are Zero-Shot Context-Aware Simultaneous Translators, by Roman Koshkin and 2 other authors
View PDFHTML (experimental)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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2406.13476 [cs.CL] |
| (orarXiv:2406.13476v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2406.13476 arXiv-issued DOI via DataCite |
Submission history
From: Roman Koshkin [view email][v1] Wed, 19 Jun 2024 11:57:42 UTC (7,065 KB)
[v2] Fri, 21 Jun 2024 07:21:28 UTC (7,067 KB)
[v3] Tue, 25 Jun 2024 04:45:19 UTC (7,067 KB)
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View a PDF of the paper titled LLMs Are Zero-Shot Context-Aware Simultaneous Translators, by Roman Koshkin and 2 other authors
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