Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents (2025)

Abstract: 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.

## introduction large language models (llms) have demonstrated notable abilities in handling various natural language processing tasks and following diverse instructions effectively. one key application is machine translation, whereas previous approaches relied on specialized encoder-decoder translation models. recent studies have explored both prompting techniques @xcite @xcite and fine-tuning methods @xcite for improving llms' translation capabilities. while document-level translation remains more challenging than sentence-level tasks @xcite , recent works have explored ways to enhance the document translation ability of llms through fine-tuning on parallel datasets @xcite and various prompting techniques @xcite to improve context awareness. these approaches provide analyses of how current llms handle document-level translation. multi-turn conversation is a representative way of interacting with llms. previous works have leveraged this ability for following instructions @xcite and building agents @xcite for reasoning tasks. more recently, methods have been explored that leverage multiple segments as exemplars in a multi-turn manner to help language models with translation tasks, but these segments tend to be fixed @xcite and do not adapt to subsequent translation data. while retrieval-based approaches have been proposed @xcite , they do not make use of context from the same document. @xcite first explored the setting of inputting a document's content sentenceby-sentence in a multi-turn manner. in this paper, we extend this setting to translate documents and use paragraphs as the translation unit in each turn. specifically, we separate the document into multiple segments, and then in each conversational turn, we pass a segment combined with user instructions as input, having the model output the corresponding translated segment. llms have access to all previous turns, which helps translate the current segment. in this manner, we merely expand the conversation without modifying the prefix, allowing for caching of previous turns. this strategy has the drawback compared to single-turn strategies in that initial segments are translated with little context. thus, we propose a new variant that first presents the entire source document before conducting multi-turn translation. this gives the model access to future context that previous multi-turn approaches lack. this approach has the advantage of providing information about the full source document user: please translate below texts into {target_lang} full source document assistant: ×n user: please translate below texts into {target_lang} source segment i assistant: source segment 0 source segment n … × 𝟏 translated document source segment 0 … source segment n user: please translate below texts into {target_lang} source segment 0 assistant: user: please translate below texts into {target_lang} source segment n … translated segment 0 assistant: translated segment n user: please translate below texts into {target_lang} source segment 0 assistant: user: please translate below texts into {target_lang} source segment n … translated segment 0 assistant: translated segment n 𝑷𝒓𝒐𝒈𝒓𝒆𝒔𝒔𝒊𝒗𝒆𝒍𝒚 translated segment i 𝑷𝒓𝒐𝒈𝒓𝒆𝒔𝒔𝒊𝒗𝒆𝒍𝒚 single-turn segment-level multi-turn source-primed multi-turn document's topic and style, which might help the model generate appropriate tense and formality levels from the start. this methodology is simple, effective, and requires no additional training, applicable to any chat-capable llms. our experiments on various proprietary and open-weight llms demonstrate its effectiveness compared to the original multiturn setting and two baselines: single-turn document input and context-free segment input. bleu, comet and blonde @xcite scores show that the multi-turn conversational approach outperforms alternatives and can serve as a standard for document translation research. we also analyze how factors like domain type and document length affect this method's performance. ## conclusion source-primed multi-turn translation: 1) gives structure to the translation task, reducing omission errors; 2) provides access to the full source document context at the beginning compared to original multi-turn translation, enabling llms to improve coherence and other document-level aspects of translation; and 3) allows for caching attention keys and values from previous turns, eliminating the efficiency bottleneck of previous segment-incontext prompting strategies.