Automatic Input Rewriting Improves Translation with Large Language Models (2025)

Abstract: 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.

## introduction machine translation (mt) users and developers have long exploited the idea that some texts are easier to translate than others. for instance, guiding people to edit their inputs so that they are well formed is a cornerstone of mt literacy courses @xcite , and adopting plain language has been shown to improve the readability of translated health content @xcite . in mt research, a wealth of studies have considered pre-processing strategies to rewrite inputs, particularly for statistical mt @xcite @xcite . the growing use of large language models (llms) for translation leads us to revisit the impact of rewriting inputs on mt. on the one hand, rewriting inputs for llm translation aligns with the re-framing of mt as a multi-step process @xcite . llms have shown promise in rewriting mt outputs @xcite @xcite , and can rewrite text according to various style specifications @xcite @xcite . on the other hand, current models might already be robust to input variability, since they are trained on vast amounts of heterogeneous data @xcite , finetuned on diverse tasks @xcite and operate at a much higher quality level compared to the statistical mt systems used in previous pre-processing studies. how should inputs be rewritten for mt? the assumption that well-written texts are easier to translate drives recommendations for mt literacy, as well as the use of paraphrasing @xcite @xcite and simplification @xcite . however, can we more directly rewrite inputs so that they are easier to translate? generic translatability has been defined as "a measurement of the time and effort it takes to translate a text" @xcite . @xcite introduced a metric to quantify mt translatability based on back-translation of mt hypotheses in the source language. given recent progress in quality estimation @xcite @xcite , we propose instead to use reference-free quality estimation scores as a measure of translatability. we thus ask the following research questions: (1) can we improve mt quality from llms by rewriting inputs for style? (2) do quality estimation metrics provide useful translatability signals for input rewriting? we conduct an empirical study with 3 openweight llms for a total of 21 input rewriting methods with varying levels of mt-awareness on translation from english into german, russian and chinese, and we further evaluate the generalizability of our best performing approach on translation from english into czech, hebrew and japanese ( §4.4). our results show that simple mt-agnostic rewrites obtained by prompting llms to simplify, paraphrase, or change the style of the input, improve translatability, and that simplification most reliably improves translation quality. interestingly, these mt-agnostic rewrites are more effective than task-aware rewrites, where llms are prompted to rewrite inputs for the purpose of mt ( §4.1). finally, using quality estimation signals to assess translatability at the segment level and select when to use rewrites further improves mt quality, outperforming more expensive fine-tuning strategies ( §4.2). human evaluation further confirms that simplified rewrites and their mt largely preserve the original meaning of the source and mt ( §5.3). ## input rewriting methods within the process of source rewriting, the goal of a rewrite model is to rewrite the original source sentence s into another form that is easier to translate while preserving its intended meaning. for mt-agnostic rewriting methods ( §2.1), which lacks translation-related knowledge, the rewrite model m θ can rewrite s into s ′ : on the contrary, both task-aware ( §2.2) and translatability-aware ( §2.3) rewriting methods incorporate some translation signal. for task-aware, m θ rewrites s with the information of the end-task (mt): for translatability-aware method, it rewrites with the knowledge of segment level quality estimation scores between source and the output of a specific mt system mt(t): figure 1 shows the overview of our proposed rewriting pipeline. to find the most effective m θ , we test a total of 21 input rewriting methods. ## results we first extensively compare rewrite strategies focusing on the overall translation quality achieved by mt-agnostic rewrites ( §4.1) and translatability-aware rewrites ( §4.2). to understand how rewrites change translations, we then analyze the trade-offs between translatability and meaning preservation ( §4.3). finally, we test whether the best-performing methods identified so far generalize to new language pairs ( §4.4). ## conclusion in this work, we studied the effectiveness of automatic input rewriting with llms in improving the quality of machine translation outputs. we explored a range of rewriting strategies with varying levels of mt-awareness: 1) mt-agnostic, 2) task-aware (knowledge of the end-task), and 3) translatability-aware rewrites (knowledge of translatability as measured with qe tools). our findings show that simpler texts are more translatable. however, mt-agnostic rewrites do not uniformly help translation quality ( §4.1), which motivates us to explore more selective strategies. selecting inputs based on translatability scores during inference time further boosts translation quality ( §4.2), addressing the pareto optimization challenge by striking a balance between translatability and meaning preservation ( §4.3). analysis shows that simplifying inputs also results in more readable translation outputs ( §5.1), and that input rewriting complements post-editing strategies ( §5.2). human evaluation complements our automatic metric by showing that both simplified rewrites and their corresponding mt largely preserve the original meaning of the source and mt ( §5.3). more broadly, this work suggests that llmassisted input rewriting is a promising direction for improving translations. the approaches introduced here represent a first step in this direction, and future work is needed to discover optimal rewriting strategies for a broader range of models. furthermore, in line with growing research on llm-based writing assistants @xcite , these results encourage future work on designing richer interactive approaches to translation with llms.