Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917956151148544 |
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| author | Hu, Hanxu Vamvas, Jannis Sennrich, Rico |
| author_facet | Hu, Hanxu Vamvas, Jannis Sennrich, Rico |
| contents | LLMs have paved the way for truly simple document-level machine translation, but challenges such as omission errors remain. In this paper, we study a simple method for handling document-level machine translation, by leveraging previous contexts in a multi-turn conversational manner. Specifically, by decomposing documents into segments and iteratively translating them while maintaining previous turns, this method ensures coherent translations without additional training, and can fully re-use the KV cache of previous turns thus minimizing computational overhead. We further propose a `source-primed' method that first provides the whole source document before multi-turn translation. We empirically show this multi-turn method outperforms both translating entire documents in a single turn and translating each segment independently according to multiple automatic metrics in representative LLMs, establishing a strong baseline for document-level translation using LLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10494 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents Hu, Hanxu Vamvas, Jannis Sennrich, Rico Computation and Language LLMs have paved the way for truly simple document-level machine translation, but challenges such as omission errors remain. In this paper, we study a simple method for handling document-level machine translation, by leveraging previous contexts in a multi-turn conversational manner. Specifically, by decomposing documents into segments and iteratively translating them while maintaining previous turns, this method ensures coherent translations without additional training, and can fully re-use the KV cache of previous turns thus minimizing computational overhead. We further propose a `source-primed' method that first provides the whole source document before multi-turn translation. We empirically show this multi-turn method outperforms both translating entire documents in a single turn and translating each segment independently according to multiple automatic metrics in representative LLMs, establishing a strong baseline for document-level translation using LLMs. |
| title | Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.10494 |