# Automatic Input Rewriting Improves Translation with Large Language Models

arXiv: https://arxiv.org/abs/2502.16682 · HTML: https://mlsanity.com/paper/2502.16682/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 63 references that are in the map.
- First posted on arXiv 23 Feb 2025.

## Builds on (most cited first, 26 of 26)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [LLM Evaluators Recognize and Favor Their Own Generations](https://mlsanity.com/paper/2404.13076/) (2024)
- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection](https://mlsanity.com/paper/2310.10482/) (2023)
- [Tower: An Open Multilingual Large Language Model for Translation-Related Tasks](https://mlsanity.com/paper/2402.17733/) (2024)
- [Aya 23: Open Weight Releases to Further Multilingual Progress](https://mlsanity.com/paper/2405.15032/) (2024)
- [The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation](https://mlsanity.com/paper/2308.07286/) (2023)
- [A Design Space for Intelligent and Interactive Writing Assistants](https://mlsanity.com/paper/2403.14117/) (2024)
- [Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting](https://mlsanity.com/paper/2310.09716/) (2023)
- [CoEdIT: Text Editing by Task-Specific Instruction Tuning](https://mlsanity.com/paper/2305.09857/) (2023)
- [RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting](https://mlsanity.com/paper/2305.15685/) (2023)
- [Leveraging GPT-4 for Automatic Translation Post-Editing](https://mlsanity.com/paper/2305.14878/) (2023)
- [RaFe: Ranking Feedback Improves Query Rewriting for RAG](https://mlsanity.com/paper/2405.14431/) (2024)
- [LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback](https://mlsanity.com/paper/2311.09336/) (2023)
- [Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts](https://mlsanity.com/paper/2409.06790/) (2024)
- [Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors](https://mlsanity.com/paper/2310.16924/) (2023)
- [Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations](https://mlsanity.com/paper/2404.07851/) (2024)
- [xTower: A Multilingual LLM for Explaining and Correcting Translation Errors](https://mlsanity.com/paper/2406.19482/) (2024)
- [Can Automatic Metrics Assess High-Quality Translations?](https://mlsanity.com/paper/2405.18348/) (2024)
- [STEER: Unified Style Transfer with Expert Reinforcement](https://mlsanity.com/paper/2311.07167/) (2023)
- [Improving Machine Translation with Large Language Models: A Preliminary Study with Cooperative Decoding](https://mlsanity.com/paper/2311.02851/) (2023)
- [Towards Higher Pareto Frontier in Multilingual Machine Translation](https://mlsanity.com/paper/2305.15718/) (2023)
- [Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model](https://mlsanity.com/paper/2310.06707/) (2023)

## Built on by (most cited first, 4 of 4)

- [An Interdisciplinary Approach to Human-Centered Machine Translation](https://mlsanity.com/paper/2506.13468/) (2025)
- [OasisSimp: An Open-source Asian-English Sentence Simplification Dataset](https://mlsanity.com/paper/2603.14111/) (2026)
- [Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation](https://mlsanity.com/paper/2606.08011/) (2026)
- [Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing](https://mlsanity.com/paper/2607.01964/) (2026)

## Abstract (arXiv)

Can we improve machine translation (MT) with LLMs by rewriting their inputs automatically? Users commonly rely on the intuition that well-written text is easier to translate when using off-the-shelf MT systems. LLMs can rewrite text in many ways but in the context of MT, these capabilities have been primarily exploited to rewrite outputs via post-editing. We present an empirical study of 21 input rewriting methods with 3 open-weight LLMs for translating from English into 6 target languages. We show that text simplification is the most effective MT-agnostic rewrite strategy and that it can be improved further when using quality estimation to assess translatability. Human evaluation further confirms that simplified rewrites and their MT outputs both largely preserve the original meaning of the source and MT. These results suggest LLM-assisted input rewriting as a promising direction for improving translations.