# Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents

arXiv: https://arxiv.org/abs/2503.10494 · HTML: https://mlsanity.com/paper/2503.10494/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [Prompting Large Language Model for Machine Translation: A Case Study](https://mlsanity.com/paper/2301.07069/) (2023)
- [Tower: An Open Multilingual Large Language Model for Translation-Related Tasks](https://mlsanity.com/paper/2402.17733/) (2024)
- [Document-Level Machine Translation with Large Language Models](https://mlsanity.com/paper/2304.02210/) (2023)
- [Large language models effectively leverage document-level context for literary translation, but critical errors persist](https://mlsanity.com/paper/2304.03245/) (2023)
- [Adapting Large Language Models for Document-Level Machine Translation](https://mlsanity.com/paper/2401.06468/) (2024)
- [BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation](https://mlsanity.com/paper/2103.11878/) (2021)
- [Efficiently Exploring Large Language Models for Document-Level Machine Translation with In-context Learning](https://mlsanity.com/paper/2406.07081/) (2024)
- [Context-Aware LLM Translation System Using Conversation Summarization and Dialogue History](https://mlsanity.com/paper/2410.16775/) (2024)
- [Context-aware and Style-related Incremental Decoding framework for Discourse-Level Literary Translation](https://mlsanity.com/paper/2409.16539/) (2024)
- [Findings of the WMT 2024 Shared Task on Chat Translation](https://mlsanity.com/paper/2410.11624/) (2024)

## Built on by (most cited first, 7 of 7)

- [DocHPLT: A Massively Multilingual Document-Level Translation Dataset](https://mlsanity.com/paper/2508.13079/) (2025)
- [Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation](https://mlsanity.com/paper/2603.25183/) (2026)
- [What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translation](https://mlsanity.com/paper/2605.13368/) (2026)
- [Loong: A Human-Like Long Document Translation Agent with Observe-and-Act Adaptive Context Selection](https://mlsanity.com/paper/2605.30274/) (2026)
- [STAR : Sentence Translation Alignment Rate for Document-to-Document Machine Translation](https://mlsanity.com/paper/2608.27161/) (2026)
- [Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Models](https://mlsanity.com/paper/2506.07583/) (2025)
- [Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking](https://mlsanity.com/paper/2609.12674/) (2026)

## Abstract (arXiv)

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.