Venue: NAACL 2025

# Automatic Input Rewriting Improves Translation

with Large Language Models

 Dayeon Ki

University of Maryland

dayeonki@umd.edu &Marine Carpuat

University of Maryland

marine@cs.umd.edu

###### 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.1

Footnote 1: We release our code and dataset at [https://github.com/dayeonki/rewrite_mt](https://github.com/dayeonki/rewrite_mt).

+
Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)_, pages 10829–10856

## 1 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 Bowker (2021); Steigerwald et al. (2022), and adopting plain language has been shown to improve the readability of translated health content Rossetti (2019). In MT research, a wealth of studies have considered pre-processing strategies to rewrite inputs, particularly for statistical MT Xia and McCord (2004); Callison-Burch et al. (2006); Stajner and Popovic (2016).

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 Briakou et al. (2024a). LLMs have shown promise in rewriting MT outputs Zeng et al. (2024); Ki and Carpuat (2024); Xu et al. (2024), and can rewrite text according to various style specifications Raheja et al. (2023); Hallinan et al. (2023); Shu et al. (2024); Krishna et al. (2024). On the other hand, current models might already be robust to input variability, since they are trained on vast amounts of heterogeneous data Touvron et al. (2023), fine-tuned on diverse tasks Raffel et al. (2020); Alves et al. (2024) 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 Callison-Burch et al. (2006); Mirkin et al. (2009); Marton et al. (2009); Aziz et al. (2010) and simplification Stajner and Popovic (2016); Stajner and Popovic (2019). 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" Kumbyr et al. (1994). Uchimoto et al. (2005) introduced a metric to quantify MT translatability based on back-translation of MT hypotheses in the source language. Given recent progress in quality estimation Fernandes et al. (2023); Naskar et al. (2023); Tomani et al. (2024), we propose instead to use reference-free quality estimation scores as a measure of translatability.

We thus ask the following research questions:

* Can we improve MT quality from LLMs by rewriting inputs for style?
* Do quality estimation metrics provide useful translatability signals for input rewriting?

We conduct an empirical study with 3 open-weight 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 (SS4.4). 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 (SS4.1). 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 (SS4.2). Human evaluation further confirms that simplified rewrites and their MT largely preserve the original meaning of the source and MT (SS5.3).

## 2 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 (SS2.1), which lacks translation-related knowledge, the rewrite model \({\mathcal{M}_{\theta}}\) can rewrite \(s\) into \(s^{\prime}\):

\[s^{\prime}={\mathcal{M}_{\theta}}(s) \tag{1}\]

On the contrary, both **Task-Aware** (SS2.2) and **Translatability-Aware** (SS2.3) rewriting methods incorporate some translation signal. For Task-Aware, \({\mathcal{M}_{\theta}}\) rewrites \(s\) with the information of the end-task (MT):

\[s^{\prime}={\mathcal{M}_{\theta}}(s,\text{MT task}) \tag{2}\]

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(\(t\)):

\[s^{\prime}={\mathcal{M}_{\theta}}(s,\text{xCOMET}(s,\text{MT}(t))) \tag{3}\]

Figure 1 shows the overview of our proposed rewriting pipeline. To find the most effective \({\mathcal{M}_{\theta}}\), we test a total of 21 input rewriting methods.

### MT-Agnostic Rewriting

MT-agnostic rewriting methods reflect various a priori assumptions on what makes text easier to translate. They do not take as input any signal of translatability or knowledge about the end-task. We consider three prompting variants here, all inspired by prior works on source rewriting [16, 8, 9].

Simplification.Simplification includes replacing complex words with simpler ones, rephrasing complex syntactic structures, and shortening sentences [7, 10]. Prior works show that simplified inputs are more conducive to MT, and particularly improve the fluency of MT outputs [8].

Paraphrase.Paraphrases are alternative ways of expressing the same information within one language, which can help resolve unknown or complex words [7]. Paraphrasing with LLMs might benefit MT by normalizing inputs using language patterns that are more frequent in LLM training data. Further, some LLMs, such

Figure 1: Overview of the rewriting pipeline. **(1) Rewrite:** Given source sentence, we generate rewrites using different rewriting methods: MT Agnostic, Task-Aware and Translation-Aware. **(2) Translate:** We translate each rewrite using our MT system, Tower-Instruct 7B. **(3) Evaluate:** We automatically evaluate rewrites along translatability, meaning preservation, and overall translation quality.

 

as TowerAlves et al. (2024), are fine-tuned on both paraphrasing and MT tasks, and might thus produce paraphrases that are useful for MT.

Stylistic.We employ an off-the-shelf text editing tool CoEdIT-XL Raheja et al. (2023) to rewrite inputs according to diverse style specifications:

* **Grammar**: Fix the grammar.
* **Coherent**: Make the text more coherent.
* **Understandable**: Make it easier to understand.
* **Formal**: Rewrite the text more formally.

These operationalize the assumption that well-formed text is easier to translate. All prompt templates are shown in Appendix Table 5.

### Task-Aware Rewriting

For task-aware rewriting methods, we design prompts that account for the fact that rewrites are aimed at MT. Prior work has shown that LLMs can post-edit errors in MT outputs Ki and Carpuat (2024); Zeng et al. (2024); Treviso et al. (2024); Xu et al. (2024); Briakou et al. (2024), raising the question of whether this ability can be extended to rewriting inputs to enhance translatability. Additionally, Tower-Instruct has been jointly trained on paraphrasing, grammatical error correction (GEC), and translation tasks, suggesting it may be well-suited for performing translatability rewrites in a zero-shot fashion. We consider two prompting strategies (Refer to Appendix Table 5 for exact templates):

Easy Translation.We prompt LLMs to rewrite inputs in a way that specifically facilitates translation into the target language.

Chain of Thought Rewrite+Translate.We use a Chain of Thought Wei et al. (2023), CoT) style prompt where LLMs are prompted to handle the entire rewriting and translation process in one sequence of CoT instructions within a single model.

### Translatability-Aware Rewriting

We propose to use quality estimation scores for a given input and output pair to assess the translatability of inputs at the segment level. This makes it possible to inject translatability signals at inference or training time. We introduce a lightweight inference-time selection strategy, and contrast it against a more expensive fine-tuning approach.

Inference-Time Selection.Input segments might not benefit from rewriting uniformly, since the quality of the original inputs and of their rewrites might vary. We thus propose to use translatability scores to decide whether or not to replace the original input with a rewrite at inference time. We use the state-of-the-art xCOMET quality estimation tool Guerreiro et al. (2024) to assess how good the translation \(t^{\prime}\) of a rewrite \(s^{\prime}\) is: \(\text{xCOMET}(s^{\prime},t^{\prime})\). We compare this score with the estimated quality of the translation \(t\) of the original source \(s\), choosing to use the rewrite if \(\text{xCOMET}(s^{\prime},t^{\prime})>\text{xCOMET}(s,t)\), and keeping the original source otherwise. This straightforward approach allows us incorporate translatability signals at inference time, with little additional cost.

Supervised Fine-tuning.The translatability-based selection process described above for inference could also be used to gather examples of good rewrites and enable instruction fine-tuning of models to rewrite text for improved translation. While designing an optimal approach for this task is out of scope for this work, we wish to compare our inference-time selection strategy with a straightforward training strategy. We construct a fine-tuning dataset of positive rewrite examples \(\mathcal{D}_{pos}\), as follows: for a given input \(s\), we generate rewrites using all MT-agnostic methods. We add to our training set the rewrites that improve translatability as measured by \(\text{xCOMET}(s^{\prime},t^{\prime})>\text{xCOMET}(s,t)\). The base LLM is then instruction fine-tuned based to rewrite input \(s\) so that it is better translated, using \(s^{\prime}\) as supervision. Detailed prompt templates are shown in Appendix A.1.

## 3 Experimental Setup

### Model & Data

MT System.We use Tower-Instruct 7B as our MT system for all our experiments since it is specifically trained for translation-related tasks and has demonstrated superior MT performance compared to other LLMs Alves et al. (2024).

Rewriting Models.For prompting experiments, we use 7B variant of three open-weight LLMs in zero-shot setting: LLaMA-2 Touvron et al. (2023) - the base model for Tower-Instruct, LLaMA-3 Grattafiori et al. (2024) - more recent multilingual model compared to LLaMA-2, and Tower-Instruct Alves et al. (2024) - the same LLM as used for our MT system. For supervised 

fine-tuning, we draw training samples from the English-German and English-Russian subset from WMT-20, 21, and 22 General MT task datasets Freitag et al. (2021)2, and provide detailed parameter settings in Appendix A.2.

Footnote 2: We do not consider English-Chinese pair here since this language pair is not supported in the dataset.

Test Data.We use the WMT-23 General MT task3 from the TowerEval dataset4 to guarantee that it was held out from the various training stages. We focus on translation from English into German (En-De), Russian (En-Ru) and Chinese (En-Zh) for an extensive empirical comparison, and then test whether the most promising approaches generalize to translation from English into Czech (En-Cs), Hebrew (En-He) and Japanese (En-Ja). See Appendix Table 7 for data statistics.

Footnote 3: [https://www2.statmt.org/wmt23/translation-task.html](https://www2.statmt.org/wmt23/translation-task.html)

Footnote 4: [https://huggingface.co/datasets/Unbabel/TowerEval-Data-v0.1](https://huggingface.co/datasets/Unbabel/TowerEval-Data-v0.1)

### Evaluation Metrics

We use xCOMET Guerreiro et al. (2024) and MetricX Juraska et al. (2023) to evaluate different aspects of rewrite quality. Specifically, we use xCOMET-XL5 and MetricX-23-XL.6 Higher scores indicate better performance for xCOMET, while lower scores are better with MetricX.

Footnote 5: [https://huggingface.co/Unbabel/XCOMET-XL](https://huggingface.co/Unbabel/XCOMET-XL)

Translatability.We quantify translatability with the quality estimation score for a specific input-output pair (xCOMET\((s^{\prime},t^{\prime})\) or MetricX-QE\((s^{\prime},t^{\prime})\)). A rewrite \(s^{\prime}\) of the original input \(s\) is considered easier to translate if xCOMET\((s^{\prime},t^{\prime})\) is higher than xCOMET\((s,t)\).

Meaning Preservation.We do not want rewrites that are easier to translate at the expense of changing the original meaning. Our meaning preservation metric evaluates how well the rewrite maintains the intended meaning of the translation as represented by the reference Graham et al. (2015). We use a reference-based metric as opposed to using the semantic similarity between \(s\) and \(s^{\prime}\) because it abstracts the meaning away from the specific formulation of \(s\), reducing overfitting. We compute xCOMET scores between the rewrites and reference translations (xCOMET\((s^{\prime},r)\)). The desired behavior is to minimize the deterioration in xCOMET\((s^{\prime},r)\) compared to xCOMET\((s,r)\).

\begin{table}
\begin{tabular}{c l c c c c c} \hline
* & 0.893 & 0.898 & 2.038 & 1.534 \\  & **MT:Agnostic** & Simplification (Transfer) & **0.915** & **0.907** & **1.504*** & 1.519 \\  & **Paraphrase d DIPERER** & **0.904** & 0.838 & **1.674** & 2.757 \\ en-de & **Task-Aware** & Easy translate (Class) & **0.901** & 0.903 & **1.759** & 2.427 \\  & **COT (Tours)** & **0.907** & 0.897 & **1.892** & 1.578 \\  & **Translatability:Aware** & Selection & **0.921*** & **0.922*** & **1.734** & **1.461*** \\  & Fine-tune (Bell) & **0.896** & 0.876 & 2.023 & 2.028 \\ \hline  & **Original** &
- & 0.872 & 0.868 & 2.535 & 2.028 \\  & **MT:Agnostic** & Simplification (Transfer) & **0.921*** & **0.891** & **1.135** & **1.921** \\  & **Paraphrase d DIPERER** & **0.904** & 0.821 & **1.249** & 3.476 \\ en-ru & Task-Aware & Easy translate (allAML) & **0.917** & **0.881** & **0.801*** & 10.401 \\  & CoT (Tours) & **0.903** & 0.875 & 2.432 & 2.024 \\  & **Translatability:Aware** & Selection & **0.914** & **0.899*** & **2.096** & **1.830*** \\  & Fine-tune (Bell) & **0.894** & 0.866 & **2.284** & 2.012 \\ \hline  & **Original** &
- & 0.786 & 0.794 & 3.445 & 2.282 \\  & **MT:Agnostic** & Simplification (Transfer) & **0.821** & **0.802** & **1.521*** & 2.227 \\ EN-ZH & **Task-Aware** & Paraphrase d DIPERERER & **0.813** & 0.722 & **1.583** & 4.009 \\  & **Task-Aware** & Easy translate (allAML) & **0.793** & 0.791 & **1.618** & 7.650 \\  & **CoT (Tours)** & **0.821** & 0.771 & **3.321** & 2.432 \\  & **Translatability-Aware** & Selection & **0.823*** & **0.819*** & **3.149** & **2.206*** \\ \hline \end{tabular}
\end{table}
Table 1: Results using different rewriting methods. Statistically significant average improvements (\(p\)-value \(<0.05\)) are **bold**. Best scores for each metric is **bold** with *. xCOMET\((s,t)\): translatability (\(\uparrow\)); xCOMET\((s,t,r)\): overall translation quality (\(\uparrow\)); MetricX\((s,t)\): quality estimation (\(\downarrow\)); MetricX\((t,r)\): reference-based metric (\(\downarrow\)). We substitute \(s\) and \(t\) to \(s^{\prime}\) and \(t^{\prime}\) when computing scores for rewrites. For each rewriting type, we show the best and worst of each methods based on xCOMET\((s,t,r)\). We abbreviate Tower-Instruct as Tower and DIPPER (L80/O60) as DIPPER due to space constraints. Full results are in Appendix B.1.
 

Translation Quality.We additionally report the combined evaluation metric, \(\textsc{xCOMET}(s^{\prime},t^{\prime},r)\) to take into account of the trade-off between the two above metrics, and MetricX\((t^{\prime},r)\) which also assesses translation quality of the rewrite but is not informed by the updated source \(s^{\prime}\).

## 4 Results

We first extensively compare rewrite strategies focusing on the overall translation quality achieved by MT-Agnostic rewrites (SS4.1) and Translatability-Aware rewrites (SS4.2). To understand how rewrites change translations, we then analyze the trade-offs between translatability and meaning preservation (SS4.3). Finally, we test whether the best-performing methods identified so far generalize to new language pairs (SS4.4).

### Simplifying Inputs Works Best

We first compare the MT Agnostic rewriting methods: simplification, paraphrasing, and stylistic edits. Due to space limits, we show the best and worst performing variations for each input rewriting method based on the overall translation quality metric xCOMET\((s,t,r)\) for each language pair in Table 1. Full results are available in Appendix B.1.

Results show that all rewriting strategies improve translatability, but only **simplification** also improves the overall translation quality. Even the lowest performing rewrites reach higher translatability than the original baseline. Each method surpasses the baseline by up to 0.056 and 0.027 \(\textsc{xCOMET}(s,t)\) average scores for En-De, up to 0.058 and 0.036 average scores for En-Ru, and up to 0.054 and 0.028 average scores for En-Zh pair. Trends are consistent with MetricX\((s,t)\). However, making inputs easier to translate often degrades quality when comparing against references \(r\). Simplification with Tower-Instruct distinguishes itself by improving translation quality based on \(\textsc{xCOMET}(s,t,r)\) scores and maintaining it according to the MetricX\((t,r)\) scores - a harder metric to improve since the reference might be biased toward the original wording of the source.

Among the three LLMs used for simplification, Tower-Instruct achieves the best translation quality, while LLaMA-3 excels in translatability at the expense of meaning preservation. Interestingly, there is no benefit to using a separate LLM, even one fine-tuned specifically on paraphrasing or style edits such as DIPPER or CoEdIT. Overall, the best performing method for MT-agnostic rewrites is simplification with Tower-Instruct, the same model we use as our MT system. We attribute this to Tower-Instruct being instruction fine-tuned on translation related tasks (but not simplification) and having more domain knowledge of the WMT dataset used in our evaluation.7

Footnote 7: [https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1)

As shown in Table 1, simplifying with Tower-Instruct still holds the top spot when compared to Task-Aware rewriting methods, as indicated by higher \(\textsc{xCOMET}(s,t,r)\) scores. This suggests that injecting knowledge about the end-task (MT) to LLMs is less effective than simplifying inputs to improve translation quality.

Overall, these results confirm the intuition that simpler text is easier to translate, but establish that rewrites are not uniformly helpful for translation quality, motivating the need for more selective input rewriting strategies.

### Selection via Translatability Improves MT

We evaluate the impact of inference-time selection based on translatability scores (_Selection_ in Table 1), and compare it further with the more expensive supervised fine-tuning strategy (_Fine-tune_).

All language pairs consistently benefit from selection. Translation quality improves significantly, with average \(\textsc{xCOMET}(s,t,r)\) gains of 0.024 for En-De, 0.031 for En-Ru, and 0.025 for En-Zh, marking the best performance among all variants. MetricX\((t,r)\) scores confirm this trend, showing average improvements of 0.073 for En-De, 0.198 for En-Ru, and 0.076 for En-Zh. At the segment level, rewrites are preferred to original inputs in 1197/1557 cases for En-De, 1610/2074 cases for En-Ru, and 2163/3074 cases for En-Zh. Fine-tuning shows smaller gains compared to MTS-Agnostic or Task-Aware methods, both in terms of translatability and translation quality, despite being more resource-intensive.

In summary, the results suggest that inference-time selection of inputs based on translatability scores is a promising strategy, outperforming MT-agnostic rewrites and rewrites obtained via a more expensive fine-tuning process.

### Input Rewriting Trades Off

#### Translatability and Meaning Preservation

We observe a moderate negative correlation between translatability and meaning preservation 

scores, with Pearson coefficients of -0.48, -0.66, and -0.52 for En-De, En-Ru, and En-Zh, respectively. This trade-off between the two metrics poses a Pareto optimization challenge: when a rewrite is easier to translate, it often results in lower meaning preservation. Therefore, we aim to find Pareto optimal solutions, which balance these trade-offs on a Pareto frontier Huang et al. (2023).8

Footnote 8: In Pareto optimization, Pareto optimal solutions are those where no single solution outperforms another in all tasks Chen et al. (2024a). The set of Pareto optimal solutions forms the Pareto frontier.

In Figure 2, we visualize our two objectives, translatability and meaning preservation, on each axis and identify the Pareto frontier. The results are consistent with the overall translation quality metric, xCOMET\((s,t,r)\), where the scores for rewriting methods on the Pareto frontier are consistently the same as or on par with the original baseline. This also aligns with our earlier findings from comparing MT-Agnostic and Task-Aware rewrites (SS4.1), where simplification with Tower-Instruct lies on the Pareto frontier for En-De and En-Ru. Even for En-Zh, although this does not lie on the frontier, it has a higher xCOMET\((s,t,r)\) score (0.802) than the original baseline (0.794). Furthermore, the best rewriting method according to xCOMET\((s,t,r)\), translatability-based selection (SS4.2), always lies on the Pareto frontier across all language pairs.

### Best Input Rewriting Strategy Improves MT on Held-out Test sets

We evaluate whether the top methods that have emerged from the controlled empirical comparison conducted so far generalize to further test settings. As shown in Table 2, we test both simplification with Tower-Instruct (_Simplification_) and translatability-based input selection (_Selection_) on new test sets from the WMT-23 General MT task, English-Czech (En-Cs), English-Hebrew (En-He), and English-Japanese (En-Ja) to assess generalization to lower-resource target languages.

Both simplification and translatability-based selection lead to progressive improvements in translation quality, as measured by xCOMET\((s,t,r)\). Notably, the selection strategy tends to excel in language pairs with lower-resource target languages, showing translation quality gains of 0.064, 0.043, 0.051 scores for En-Cs, En-He, En-Ja, respectively, compared to increases of 0.017, 0.031, and 0.025 for En-De, En-Ru and En-Zh. At the segment level, rewrites are also more preferred over original inputs, selected in 1395/2074 cases for En-Cs, 1309/2074 for En-HE, and 1411/2074 for En-Ja. MetricX trends are consistent.

In sum, our findings generalize well to held-out test sets, further validating the effectiveness of the translatability-based selection strategy. This approach offers a practical and scalable solution for

Figure 2: Pareto frontier per language pair. For each subplot, the \(x\)-axis is the translatability and \(y\)-axis is the meaning preservation scores. Pareto frontier **(dashed line)** visualizes the optimal solutions that take into account the trade-off between the two metrics. Each shape represents different rewriting methods and each color represent specific prompt or model variation.

 

input rewriting across a broader range of domains and language pairs, though there are many other dimensions that remain unexplored. We have conducted initial experiments with additional LLMs and source languages, shown in Appendix D.1 and D.2, which confirms our previous findings that simplification rewriting enhances translation quality. We leave a more comprehensive exploration of this direction for future work.

## 5 Analysis

### Simplifying Inputs Improves MT Readability

Simplification as an input rewriting strategy can balance translatability and meaning preservation, leading to overall improvements in translation quality. We also examine whether this enhances the readability of both inputs and, subsequently, translation outputs. In Table 3, we present the Flesch Reading Ease score9 and Gunning Fog index10 to measure input readability, and the Vienna formula (WSTF) Zowalla et al. (2023) and the Russian version of Flesch Readability test Solnyshkina et al. (2018) to assess output readability for En-De and En-Ru, respectively.

Footnote 9: [https://en.wikipedia.org/wiki/Flesch-Kincaid_readability_tests](https://en.wikipedia.org/wiki/Flesch-Kincaid_readability_tests)

Footnote 10: [https://en.wikipedia.org/wiki/Gunning_fog_index](https://en.wikipedia.org/wiki/Gunning_fog_index)

As expected, input readability improves across all simplification methods, whether used in MT-Agnostic LLaMA-2, LLaMA-3, and TowerInstruct in Table 3) or Translatability-Aware Selection in Table 3) manner. Interestingly, simplification not only leads to more readable input but also more readable outputs, with gains of up to \(0.22\) WSTF scores for En-De and \(0.95\) Flesch scores for En-Ru. We provide several qualitative examples in Appendix Tables 13 to 15 that illustrate how simplification rewrites can lead to varying degrees of readability improvements in both inputs and translation outputs.

### Input Rewriting outperforms Post-editing

The symmetric task to input rewriting is post-editing, which focuses on improving and correcting errors in translation outputs. Can post-editing alone achieve the same improvements, or are both strategies _complementary_? To explore this, we compare input rewriting to post-editing by prompting Tower-Instruct11 to simplify either inputs or outputs. As shown in Table 4, rewriting inputs (**I**) offers a notable advantage over post-editing outputs (**Owo**), even when post-editing is guided by the input sentence (**Ow**). Combining input rewriting and post-editing (**I+O**) yields the highest translation quality, though the difference compared to input rewriting alone is not statistically significant. This confirms that rewriting text for better translatability before translation plays a more decisive role than post-editing the output.12

Footnote 11: We focus on Tower-Instruct as it is a multilingual LLM capable of rewriting in non-English target languages.

\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Language** & **Type** & \(\mathbf{x}(s,t)\) & \(\mathbf{x}(s,t,r)\) & \(\mathbf{M}(s,t)\) & \(\mathbf{M}(t,r)\) \\ \hline  & Original & 0.893 & 0.898 & 2.038 & 1.534 \\  & I & **0.922** & **0.907** & **1.504** & 1.519 \\
**En-de** & **Owo** & 0.863 & 0.879 & 2.941 & 2.200 \\  & **Ow** & 0.879 & 0.894 & 2.515 & 1.858 \\  & **I+O** & 0.915 & **0.907** & 1.751 & **1.502** \\ \hline  & Original & 0.861 & 0.854 & 2.535 & 2.028 \\  & **I** & **0.921** & 0.891 & **1.135** & **1.921** \\
**EN-RU** & **Owo** & 0.868 & 0.864 & 2.815 & 2.384 \\  & **Ow** & 0.872 & 0.869 & 2.674 & 2.259 \\  & **I+O** & 0.917 & **0.892** & 1.632 & 2.045 \\ \hline  & Original & 0.786 & 0.794 & 3.445 & **2.282** \\  & **I** & **0.821** & 0.802 & **1.521** & 2.327 \\
**EN-ZH** & **Owo** & 0.713 & 0.751 & 5.585 & 4.262 \\  & **Ow** & 0.746 & 0.780 & 4.363 & 2.676 \\  & **I+O** & 0.818 & **0.804** & 3.335 & 2.323 \\ \hline \end{tabular}
\end{table}
Table 4: Results for input rewriting (**I**), post-editing output without source signal (**Owo**), with source signal (**Ow**), and the combination of both strategies (**I+O**). Best scores for each metric is **bold**. We use the same abbreviations for metrics as in Table 2.

\begin{table}
\begin{tabular}{c l r r r r} \hline \hline
* \\  & LLAMA-2 & 66.69 & 9.25 & 1.15 &
* \\  & Towfire-Instruct & **68.17** & **8.99** & **1.13** &
- \\  & Selection & 63.27 & 10.09 & 1.26 &
- \\ \hline  & Original & 69.93 & 9.91 &
- & 65.67 \\  & LLAMA-2 & **74.73** & 8.37 &
- & **66.62** \\
* & 66.36 \\  & Tower-Instruct & 74.14 & **8.19** &
- & 65.40 \\  & Section & 72.24 & 9.37 &
- & 65.89 \\ \hline  & Original & 66.51 & 10.08 &
- \\  & LLAMA-2 & 71.64 & 8.74 &

- \\  & Towfire-Instruct & **72.22** & **8.42** &
- \\  & Selection & 68.41 & 9.68 &
- \\ \hline \hline \end{tabular}
\end{table}
Table 3: Input and output readability scores for simplification rewriting method. **Flesch**: Flesch Reading Ease score (\(\uparrow\)); **GFI**: Gunning Fog Index (\(\downarrow\)); **WSTF**: Vienna formula (\(\downarrow\)); **Flesch-Ru**: Russian version of Flesch (\(\uparrow\)).
 



### Human Evaluation

Original MT vs. Rewrite MT.We conduct a manual evaluation to determine whether bilingual human annotators rate translations generated using our winning rewrite method (simplification with Tower-Instruct) as superior to the original translations. For each language pairs (En-De, En-Ru, En-Zh), we randomly select 20 pairs of instances, resulting in a total of 180 annotations from three annotators per pair. Inter-annotator agreement, measured by Fleiss' Kappa13, is moderate, with values of 0.43, 0.39, and 0.51 for En-De, En-Ru, and En-Zh, respectively. For each instance, annotators are first provided with two translations and asked to evaluate on three axes: **1) Fluency, 2)** Understandability, and **3)** Level of detail. Subsequently, we provide the reference translation, and annotators are asked to assess **4)** Meaning preservation. Annotators are also given the option to provide free form comments. Further details on the annotation set-up are available in Appendix E.1.

Footnote 13: [https://en.wikipedia.org/wiki/Fleiss_kappa](https://en.wikipedia.org/wiki/Fleiss_kappa)

As illustrated in Figure 3, the human evaluation results confirm that translations from simplified inputs are rated as more fluent, understandable, and better at preserving the meaning of the reference translation. While this improvement is clear for the En-De and En-Zh pair, for En-Ru pair, annotators rate original MT as more fluent and more faithful to the original meaning.14 Some En-Ru annotators who preferred the original MT noted that it often retained a more accurate sense of the words in the reference. In contrast, those who favored the simplified rewrite MT highlighted that translations are more contextually appropriate, easier to read, and more comprehensible than the original MT.

Footnote 14: Note that the Fleiss’s Kappa scores indicate that there is more disagreement between annotators for En-Ru pair.

Original vs. Rewrite.Our automatic meaning preservation metric evaluates the extent to which the original meaning is retained in the rewrite by comparing the rewritten source to the reference translation, rather than to the original source Graham et al. (2015). Comparing to the original source is in the same language, but introduces a bias toward the original wording. On the other hand, comparing to the reference involves a cross-lingual comparison and is affected by unstable quality of references Kocmi et al. (2022), but is less biased toward the original wording of the source.

To complement our automatic metric, we conduct a manual evaluation to assess how well the rewrites from simplification with Tower-Instruct preserve the meaning of the original source. We randomly sample 30 pairs of instances and collect three annotations per pair, totaling 90 annotations. Annotators are presented with both the original and rewritten sources and asked to evaluate how well the rewrite captures the meaning of the original source using a 4-point Likert scale (1: Does not capture meaning, 2: Partially, 3: Mostly, 4: Fully). Inter-annotator agreement by Fleiss' Kappa is 0.45. Of the 90 annotations, 55 were rated as 4, 27 as 3, 7 as 2, and 1 as 1, resulting an average score of 3.51. These results indicate that simplified rewrites generated by Tower-Instruct, although compared against the original source, still largely preserve the original meaning. Further details are provided in Appendix E.2.

## 6 Related Work

Rewriting with LLMs.Recent advances in LLMs have demonstrated impressive zero-shot capabilities in rewriting textual input based on user requirements Shu et al. (2024). Most LLM-assisted rewriting tasks focus on query rewriting Efthimi

Figure 3: Win rates for human evaluation comparing Original MT vs. Rewrite MT across three language pairs (En-De, En-Ru, En-Zh) and four evaluation criteria: Fluency, Understandability, Level of detail (Detailed), and Meaning preservation relative to the reference translation.



adis, 1996), which aims to reformulate text-based queries to enhance their representativeness and improve recall with retrieval-augmented LLMs Mao et al. (2023); Zhu et al. (2024). Rewriting methods include prompting LLMs both as rewriters and rewrite editors Ye et al. (2023); Kunilovskaya et al. (2024), and training LLMs as rewriters using feedback alignment learning Ma et al. (2023); Mao et al. (2024). Another line of work focuses on style transfer, where the goal is to rewrite textual input into a specified style Yuan et al. (2022); Hallinan et al. (2023). Our research aligns with efforts to rewrite texts with LLM assistance; however, unlike these works, we focus on rewriting source inputs to enhance MT quality.

Quality Estimation Metrics.The discrepancy between lexical-based metrics (e.g., BLEU Papineni et al. (2002), chrF Popovic (2015)) and human judgments Ma et al. (2019) has led to research in _neural_ metrics. Particularly, quality estimation (QE) metrics, which compute a quality score for the translation conditioned only on the source sentence, have demonstrated benefits in improving MT quality. QE metrics are used for various purposes, including filtering out low-quality translations during training Tomani et al. (2024), applying to post-editing workflows Bechara et al. (2021), and providing feedback to users of MT systems Mehandru et al. (2023). In our experiments, we use xCOMET as our main evaluation metric, as it shows the best correlation with human judgments Agrawal et al. (2024). We primarily use xCOMET as a QE metric to compute translatability, further providing this information as knowledge to LLMs to improve MT quality.

Rewriting MT Outputs.The symmetric task of post-editing MT outputs has received significantly more attention than rewriting MT inputs. Most recent work relies on LLMs to automatically detect and correct errors in MT outputs using their internal knowledge Raunak et al. (2023); Zeng et al. (2024); Chen et al. (2024), with the help of external feedback Ki and Carpuat (2024); Xu et al. (2024) or through fine-tuning Treviso et al. (2024). In contrast, the task of rewriting MT inputs to make them more suitable for translation has been relatively underexplored with LLMs. While there have been some efforts in query rewriting and style transfer to improve retrieval Mao et al. (2023); Zhu et al. (2024) and stylistic coherence Ye et al. (2023); Hallinan et al. (2023), the specific application of LLMs to rewrite inputs for the purpose of enhancing MT quality is still emerging. Our research addresses this gap by focusing on the potential of LLM-assisted input rewriting to improve the translatability and quality of the resulting translations.

## 7 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 (SS4.1), which motivates us to explore more selective strategies. Selecting inputs based on translatability scores during inference time further boosts translation quality (SS4.2), addressing the Pareto optimization challenge by striking a balance between translatability and meaning preservation (SS4.3). Analysis shows that simplifying inputs also results in more readable translation outputs (SS5.1), and that input rewriting complements post-editing strategies (SS5.2). 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 (SS5.3).

More broadly, this work suggests that LLM-assisted 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 Lee et al. (2024), these results encourage future work on designing richer interactive approaches to translation with LLMs.

## 8 Limitations

We focus our investigation on Tower-Instruct 7B as our MT system, as it is an open-weight model. We exclude closed and larger models such as GPT-415 in the current experiments.

Footnote 15: [https://openai.com/index/gpt-4/](https://openai.com/index/gpt-4/) 

2023; Ye et al., 2023; Shu et al., 2024; Mao et al., 2024), and using English as the source language benefits performance from its prevalence in LLM training data. One critical area of future research lies in developing rewriting tools that support a wider range of languages beyond English.

## 9 Acknowledgement

We thank the anonymous reviewers and the members of the clip lab at University of Maryland for their constructive feedback. This work was supported in part by NSF Fairness in AI Grant 2147292, by the Institute for Trustworthy AI in Law and Society (TRAILS), which is supported by the National Science Foundation under Award No. 2229885, and by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via the HIATUS Program contract #2022-22072200006, by NSF grant 2147292. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, NSF or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.

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## Appendix A Model and Experiment Details

### Prompt Templates

In Tables 5 and 6, we describe the prompt templates used for prompting and fine-tuning experiments, respectively. For stylistic rewriting, we use the same prompts as those used to train the CoEdIT-XL model. During prompting, we provide the original source as the input, while for fine-tuning, we provide the positive rewrite along with the source.

### Training Setup

All models are trained using one NVIDIA RTX A5000 GPU. In practice, we find that fine-tuning converges in around 3 hours. We use a 90/10 train/validation data split and adopt QLoRA Dettmers et al. (2023), a quantized version of LoRA Hu et al. (2022), for parameter-efficient training. We train Tower-Instruct 7B with 8-bit quantization, a LoRA rank of 16, a scaling parameter (\(\alpha\)) of 32, and a dropout probability of 0.05 for layers. We train for 10 epochs. All unspecified hyperparameters are set to default values.

### Decoding Strategy

We use greedy decoding (no sampling) when generating rewrites for prompting experiments. We fix the temperature value to 0 throughout the experiments in order to eliminate sampling variations.

### Dataset Details

We provide detailed statistics of our training (\({\cal D}_{pos}\)) and test dataset in Table 7. For \({\cal D}_{pos}\), we only use rewrites where the xCOMET\((s^{\prime},t^{\prime})\) score is higher than the original xCOMET\((s,t)\) score. We further conduct a two-step pre-processing procedure: **1)** Remove duplicate instances and **2)** Remove lengthy instances where the upper threshold is set as Q\(3+1.5\times\text{IQR}\).

## Appendix B Detailed Results

### Full Results

In Tables 8 to 10, we present the detailed numerical results for all tested variations. Most rewrites yield higher xCOMET\((s,t)\) scores, indicating better translatability compared to the original baseline. For stylistic rewrites with CoEDIT, prompting to make the text easier to understand (Understandable) achieves the highest translatability score, while prompting to rewrite the text more formally (Formal) results in the highest translation quality. The Coherent prompt achieves the highest meaning preservation score but this is because most rewrites are merely copies of the original source (Appendix C.1). Overall, we demonstrate that translatability-based selection method remains the most effective method, even outperforming scores from our fine-tuned LLMs.

### Impact of LLM

Among the three LLMs used for prompting, Tower-Instruct performs the best in terms of the combined metric xCOMET\((s,t,r)\). Although it lags behind LLAMA-2 and LLAMA-3 in translatability, its meaning preservation score deteriorates the least, resulting in the highest overall score. LLAMA-3 performs the best in terms of translatability, likely due to its more multilingual training data, with over 5% of its pre-training dataset consisting of high-quality non-English data.16 This suggests that the amount of multilingual data in the pre-training phase may enhance the model's ability to generate more translatable rewrites. However, 

this advantage does not extend when comparing the LLAMA models to Tower-Instruct. Despite being inherently multilingual primarily trained on translation-related tasks, Tower-Instruct performs lower than the LLaMA models in translatability. This discrepancy can be attributed to Tower-Instruct not being specifically trained on rewriting tasks to improve MT quality, highlighting the importance of introducing translational knowledge for effective rewriting.

We further compare the results with off-the-shelf paraphrasing (DIPPER) and text-editing (CoEDIT-XL) tools. Despite being specifically trained for rewriting tasks, their rewrites are not as translatable as those generated by the prompted LLMs. For DIPPER, this may be due to its primary focus on paraphrasing, which has been shown to be less effective (SS4.1). In the case of CoEDIT, we attribute the lower performance to the model's smaller size (3B) compared to the 7B LLMs used for prompting.

### Same LLM vs. Different LLM

We distinguish whether the LLM being prompted is the same as the one used as the MT system. Initially, we expected the highest improvements when prompting Tower-Instruct, which may incur self-preference bias, where the LLM favors its own outputs due to recognition Panickssey et al. (2024). However, our results indicate that prompting Tower-Instruct does not yield the most translatable rewrites. Instead, the LLaMA series models consistently outperform in this aspect. Interestingly, Tower-Instruct consistently produces rewrites that are more meaning-preserving compared to LLaMA-2 or LLaMA-3, resulting in higher xCOMET\((s,t,r)\) scores overall. We conclude that prompting the same LLM used for the MT system is not helpful in generating more translatable rewrites, but these rewrites are better at preserving the intended meaning.

## Appendix C Qualitative Evaluation Details

### Copying Behavior

To prevent LLMs from directly copying the original source, we explicitly state in the prompt to "_avoid directly copying the source_" (Appendix A.1). However, we still observe some rewrites that are identical to the source sentence. We count the occurrences and compute the percentage per language pair in Table 11. Note that we do not consider Translatability-Aware Selection rewrite method here since this involves selecting whether to keep the original source or use the rewrite based on translatability scores. The highest occurrence appears for stylistic rewrites using the CoEDIT-XL Coherent prompt, where the source is copied most of the time (82.2%, 91.9%, 93.2% for En-De, En-Ru, and En-Zh, respectively).

### What makes a Good Rewrite for MT?

Qualitatively examining translation outputs reveals several common patterns, which motivate us to conduct a detailed qualitative analysis. Here, we aim to identify the properties that lead to meaning-equivalent rewrites that are easier to translate. We examine 200 data instances where each rewrite is the highest performing rewrite based on the xCOMET\((s,t)\) score. To focus on successful rewrites, we filter instances where xCOMET\((s^{\prime},t^{\prime})>\text{xCOMET}(s,t)\). Each rewrite is annotated with the following labels: (1) **Simplified**: Replaces complex words with simpler ones or reduces structural complexity; (2) **Detailed**: Adds information for better context; (3) **Fluency**: Restructures the sentence for better flow and readability. Examples of rewrites for each annotation label are in Table 12.

As shown in Figure 4, most successful rewrites are labeled as **Simplified**. This highlights the effectiveness of simplification, which has been consistently effective even in the context of LLMs. Notably, many simplified rewrites involve changing complex words to simpler, more conventional alternatives (e.g., "Derry City _emerged victorious_ in the President's Cup as they _ran out_ 2-0 _win

Figure 4: Distribution of properties of good rewrites.

 

ners_ over Shamrock Rovers." \(\rightarrow\) 'Derry City _won_ the President's Cup title by _defeating_ Shamrock Rovers 2-0."). This finding aligns with our conclusions from MT-Agnostic rewriting methods (SS2.1), where simplification emerged as the best rewrite method among the prompting variations.

## Appendix D Additional Results

### Additional LLM Baselines

LLMs for Rewriting.Our initial experiments consist of 21 input rewriting methods across 3 LLMs (LLaMA-27B, LLaMA-38B, and Tower-Instruct7B). In Table 16, we present extended experiment results by applying simplification rewriting with two additional LLMs: Aya-23 8B [4] and Tower-Instruct 13B [5]. The results confirms that simplification rewriting improves translation quality measured by xCOMET\((s,t,r)\) compared to the original baseline.

LLMs for MT.Furthermore, we initially relied on Tower-Instruct7B as our MT system for all our experiments since it is specifically trained for translation-related tasks and has demonstrated superior MT performance (SS2). However, we extend our analysis by comparing the original baseline and our winning strategy (simplification with Tower-Instruct7B) using two additional LLMs as the MT system. As shown in Table 17, our method outperforms the original baseline in terms of both the translation quality (xCOMET\((s,t,r)\)) and MetricX\((s,t)\), regardless of the LLM used as the MT system.

### Additional Language Pairs

To assess the generalizability to other source languages, we test two of our winning strategies (simplification with Tower-Instruct7B and inference-time selection) on seven additional intongLighish and non-English language pairs from the WMT-23 General MT task test set.17 As shown in Table 18, while translatability scores (xCOMET\((s,t)\)) improve across all language pairs, translation quality (xCOMET\((s,t,r)\)) improvements are less pronounced compared to out-of-English pairs. Notably, gains in translation quality are observed only for German-English (De-En) and Chinese-English (Zh-En) pairs. These results highlight the importance of input rewrites' quality, which is currently higher for high-resource source languages. This motivates further work to strengthen input rewriting for broader range of source languages.

Footnote 17: [https://www2.statmt.org/wmtz3/translation-task.html](https://www2.statmt.org/wmtz3/translation-task.html)

## Appendix E Human Annotation Details

We use Qualtlrics18 to design our survey and Prolific19 to recruit human annotators fluent in the tested target language.

Footnote 18: [https://www.qualtrics.com](https://www.qualtrics.com)

Footnote 19: [https://www.prolific.com](https://www.prolific.com)

### Original MT vs. Rewrite MT Details

We randomize the order of the two sentences (original MT and rewrite MT) to mitigate position bias. Annotators evaluate which sentence is better across four dimensions: fluency, understandability, level of detail, and meaning preservation. The entire survey is estimated to take approximately 20 minutes to complete. We recruit a total of 9 annotators and provide a compensation of 5 US dollars per survey (15 US dollars/hr), totaling 45 US dollars.

### Original vs. Rewrite Details

Each annotator is tasked to judge how well the rewritten sentence preserves the meaning of the original source sentence. The survey is estimated to take approximately 30 minutes to complete. We recruit a total of 3 annotators. We offer a compensation of 7.5 US dollars per survey (15 US dollars/hr), totaling 22.5 US dollars.

### Annotator Instructions

In Figures 5 to 8, we present the instructions and survey content provided to annotators. For the Original MT vs. Rewrite MT evaluation, each annotator reviews 20 sets of examples. Each question consists of two parts: **1)** comparing the two sentences based on fluency, understandability, and level of detail, and **2)** selecting which sentence better reserves the meaning of the reference translation. For the Original vs. Rewrite evaluation, each annotator reviews 30 sets of examples. Additionally, a free-form text box is provided alongside each example for annotators to offer feedback or suggestions.

## Appendix F Time & Computational Efficiency

We show that on average, rewriting with our winning strategy is not a resource-intensive option for downstream applications in terms of both time and 

computation. For approximately 1.5K sentences, the rewrite and MT pipeline using our winning strategy (simplification with Tower-Instruct 7b takes 1 hour, compared to 30 minutes for the MT process alone. All variants of our prompting experiments are conducted using a single NVIDIA RTX958 A5000 GPU. In terms of efficiency compared to automatic post-editing (SS5.2), both approaches remains equivalent in time and computational requirements since the rewriting or post-editing process only differs in its position within the pipeline. Input rewriting modifies the source before the MT system, while output post-editing adjusts the translation after the MT system.

 



\begin{table}
\begin{tabular}{l l} \hline \hline
**Basic** & \\ \hline
***Instruction: Rewrite this English sentence to give a better translation.\(\backslash\)n\(\backslash\)n \\
***English: This is a very nice skirt. The lazy pattern is classy and soft.\(\backslash\)n \\
***English rewrite: The lack pattern on this skirt is elegant and soft.\(\backslash\)n \\ \hline \hline
**MT** & \\ \hline
***Institution: Rewrite this English sentence to give a better translation in German. German sentence is the hypothesis translation that we are trying to improve.\(\backslash\)n\(\backslash\)n \\
***English: This is a very nice skirt. The lazy pattern is classy and soft.\(\backslash\)n \\
***German: Das is it ein sehr schöner Rohre. Das schicke Spitzenmuster ist wich und elegant.\(\backslash\)n \\
***English rewrite: The lazy pattern on this skirt is elegant and soft. \\ \hline \hline
**Reference** & \\ \hline
***Instruction: Rewrite this English sentence to give a better translation in German. German sentence is the human-annotated translation that we are trying to pursue.\(\backslash\)n\(\backslash\)n \\
***English: This is a very nice skirt. The lacy pattern is classy and soft.\(\backslash\)n \\
***German: Das sit ein sehr schöner Rock. Das Spitzenmuster ist silvoll und weich.\(\backslash\)n \\
***English rewrite: The lacy pattern on this skirt is elegant and soft. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Exemplar prompt templates for supervised fine-tuning experiments (English-German pair). We additionally give machine translation for the **MT** prompt and reference translation for the **Reference** prompt after **Aff# German::**

\begin{table}
\begin{tabular}{p{142.3pt} p{280.5pt}} \hline \hline
**Rewrite** & **Prompt** \\ \hline
**Simplification** & Simplify the English sentence. Simplification may include identifying complex words and replacing with simpler or shorter words or using active voice instead of passive voice. Try to keep the meaning of the Original sentence. Original: _This is a very nice skirt. The lacy pattern is classy and soft._ Simplified: \\ \hline
**Paraphrase** & Paraphrase the English sentence. Try to not directly copy but keep the meaning of the Original sentence. Original: _This is a very nice skirt. The lacy pattern is classy and soft._ \\ \hline
**Stylistic (CoEdit)** & 
\begin{tabular}{c} **(GEC)** Fix the grammar: \\ **(Coherent)** Make this text coherent: \\ **(Understanding)** Rewrite to make this easier to understand: \\ **(Formal)** Write this more formally: \\ \hline
**Easy Translation** & Rewrite the Original sentence to be easier for translation in target language. New sentence should be in English. Original: _This is a very nice skirt. The lacy pattern is classy and soft._ New: \\ \hline
**Cot T** & 
\begin{tabular}{c} **(Step 1)** Rewrite the Original English sentence to New English sentence that translates better into German. \\ Avoid directly copying the Original sentence while keeping its meaning. New sentence should be in English. Original: _This is a very nice skirt. The lacy pattern is classy and soft._ New: \\ **(Step 2)** Now, translate the English sentence to German. \\ English: German: \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Exemplar prompt templates for English-German language pair used for prompting experiments. _Italic_ represents the source sentence used in this example.





\begin{table}
\begin{tabular}{l l c r} \hline \hline
**Split** & **Dataset** & **\# Sentences** \\ \hline \multirow{3}{*}{**Train**} & Source and positive rewrite pairs for SFT (English-German, \(\mathcal{D}_{pos}\)) & 7,016 \\  & Source and positive rewrite pairs for SFT (English-Russian, \(\mathcal{D}_{pos}\)) & 8,126 \\ \hline \multirow{4}{*}{**Test**} & WMT-23 General MT Task (English-German) & 1,557 \\  & WMT-23 General MT Task (English-Russian) & 2,074 \\  & WMT-23 General MT Task (English-Chinese) & 3,074 \\  & WMT-23 General MT Task (English-Czech) & 2,074 \\  & WMT-23 General MT Task (English-Hebrew) & 2,074 \\  & WMT-23 General MT Task (English-Japanese) & 2,074 \\ \hline \end{tabular}
\end{table}
Table 7: Summary statistics of training and test datasets.

\begin{table}
\begin{tabular}{c l l l l l l} \hline \hline
* & 0.893 & 0.904 & 0.898 & 2.038 & 1.534 \\ \cline{2-7}  & **MT-Agnostic** & **Simplification** (LLAMA-2) & **0.931** & 0.846 & 0.900 & **1.185** & 1.727 \\  & & (LLAMA-3) & **0.944** & 0.820 & 0.903 & **0.925*** & 1.600 \\  & & (Towre-Instruct) & **0.922** & 0.885 & **0.907** & 1.504 & 1.519 \\  & & **Paraphing** (LLAMA-3) & **0.926** & 0.823 & 0.889 & **1.126** & **1.480** \\  & & (LLAMA-3) & **0.938** & 0.796 & 0.892 & **0.955** & **1.469** \\  & & (Towre-Instruct) & 0.902 & 0.887 & 0.901 & **1.310** & 1.534 \\  & & (DHPPER (LSWORM)) & **0.904** & 0.745 & 0.838 & **1.674** & 2.757 \\  & & (DHPPER (LSWORM)) & **0.913** & 0.797 & 0.863 & **1.461** & 2.266 \\  & & (DHPPER (LSWORM)) & **0.917** & 0.847 & 0.892 & **1.555** & 1.958 \\ \multirow{2}{*}{**EN-DE**} & **Skylastic** (ECIT GGC) & **0.901** & 0.899 & 0.900 & **1.709** & 1.555 \\  & & (CoEDT Clownet) & 0.898 & 0.900 & 0.898 & **1.728** & 1.595 \\  & & (CoEDT Unostrandable) & **0.949** & **0.758** & 0.862 & **0.989** & 2.610 \\  & & (CoEDT Formal) & **0.937** & 0.830 & 0.900 & **1.063** & 1.879 \\ \hline \multirow{2}{*}{**Task-Aware**} & **Exay Translation** (LLAMA-2) & **0.916** & 0.857 & 0.893 & **1.654** & 2.482 \\  & (LLAMA-3) & **0.932** & 0.827 & 0.899 & **1.151** & 2.241 \\  & (Twore-Instruct) & **0.901** & 0.900 & 0.903 & **1.759** & 2.427 \\  & **CoE** (Towre-Instruct) & **0.907** & 0.816 & 0.897 & **1.892** & 1.578 \\ \hline \multirow{2}{*}{**Translatability-Aware**} & **Selection** & **0.921** & 0.907 & **0.915*** & **1.734** & **1.461*** \\  & **Fine-tune** (Basic) & **0.934** & 0.851 & **0.909** & **1.878** & **1.499** \\  & (MIT) & **0.919** & 0.856 & 0.903 & **1.947** & 1.593 \\  & (Reference) & 0.896 & 0.836 & 0.876 & 2.023 & 2.028 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Detailed results of English-German pair using different rewrite methods. Statistically significant average improvements (\(p\)-value \(<0.05\)) are **bold**. Best scores for each metric is **bold** with *. \(\text{xCOMET}(s,t)\): translatability (\(\uparrow\)); \(\text{xCOMET}(s,r)\): meaning preservation (\(\uparrow\)); \(\text{xCOMET}(s,t,r)\): overall translation quality (\(\uparrow\)); \(\text{MetricX}(s,t)\): quality estimation (\(\downarrow\)); \(\text{MetricX}(t,r)\): reference-based metric (\(\downarrow\)). For DIPPER [1] variations, L and O denote lexical and order diversity, respectively.




\begin{table}
\begin{tabular}{l l c c c c c c} \hline
* & 0.786 & 0.775 & 0.794 & 3.445 & 2.282 \\ \cline{2-10}  & **MT-Agnostic** & **Simplification (LLAMA-2)** & **0.828** & 0.728 & 0.796 & **1.321** & 2.537 \\  & & (LLAMA-3) & **0.823** & 0.701 & 0.795 & **1.252*** & 2.572 \\  & & (Towards-Restruct) & **0.821** & 0.759 & **0.802** & **1.521** & **2.227** \\  & & **Paraphrase (LLAMA-2)** & **0.818** & 0.683 & 0.771 & **1.330** & 2.478 \\  & & (LAMA-3) & **0.826** & 0.662 & 0.766 & **1.341** & 2.534 \\  & & (Tower-Restruct) & **0.797** & 0.765 & 0.798 & **1.580** & 2.283 \\  & & (DIPPE (LSWOOH)) & **0.813** & 0.622 & 0.722 & **1.583** & 4.009 \\ EN-ZH & & (DIPPE (LSWOOH)) & **0.816** & 0.670 & 0.750 & **1.499** & 3.196 \\  & & (DIPPE (LSWOOH)) & **0.809** & 0.711 & 0.775 & **1.503** & 2.725 \\  & & **Stylistic (CoGeIT GEC)** & 0.789 & 0.772 & 0.795 & **1.632** & 2.251 \\  & & (CoGeIT GEC) & 0.786 & 0.774 & 0.794 & **1.658** & 2.267 \\  & & (CoGeIT Ubestrandable) & **0.839*** & 0.677 & 0.774 & **1.358** & 3.174 \\  & & (CoGeIT Foresh) & **0.832** & 0.730 & 0.798 & **1.336** & 2.443 \\ \hline
**Task-Aware** & **Fasy Translation (LLAMA-2)** & **0.821** & 0.721 & 0.784 & **1.900** & 7.732 \\  & & (LLAMA-3) & **0.830** & 0.687 & 0.783 & **1.360** & 7.608 \\  & & (TOWER-Instruct) & **0.793** & 0.762 & 0.791 & **1.618** & 7.650 \\  & & **CoGeit (Torre-Inspired)** & **0.821** & 0.769 & 0.771 & **3.321** & 2.432 \\ \hline
**Translatability-Aware** & **Selection** & **0.823** & **0.783*** & **0.819*** & **3.149** & **2.206*** \\ \end{tabular}
\end{table}
Table 10: Detailed results of English-Chinese pair using different rewrite methods.

\begin{table}
\begin{tabular}{c l c c c c c} \hline
* & 0.872 & 0.884 & 0.868 & 2.535 & 2.028 \\ \cline{2-6}  & **MT-Agnostic** & **Simplification (LLAMA-2)** & **0.916** & 0.839 & **0.882** & **0.951** & 2.160 \\  & & (LLAMA-3) & **0.919** & 0.812 & **0.885** & **0.804** & 2.039 \\  & & (Tokura-Restruct) & **0.921** & 0.870 & **0.891** & **1.135** & **1.921** \\  & & **Paaphrase (LLAMA-2)** & **0.923** & 0.804 & **0.881** & **0.882** & **1.853** \\  & & (LDAMA-3) & **0.930** & 0.788 & **0.882** & **0.855** & **1.863** \\  & & (ToWER-Restruct) & **0.887** & 0.878 & **0.878** & **1.095** & **1.976** \\  & & (DPPPE (LSWOOH)) & **0.904** & 0.735 & 0.821 & **1.249** & 3.476 \\  & & (DPPE (LSWOOH)) & **0.909** & 0.790 & 0.853 & **1.105** & 2.773 \\  & & (DPPE (LSWOOH)) & **0.905** & 0.834 & 0.873 & **1.119** & 2.418 \\ EN-RU & & **Stylistic (CoGeIT GEC)** & 0.873 & 0.884 & 0.869 & **1.327** & **1.969** \\  & & (CoGeIT Obseratorship) & **0.918** & 0.801 & 0.873 & **0.991** & 2.726 \\  & & (CoGeIT Foresh) & **0.916** & 0.841 & **0.887** & **0.922** & 2.020 \\ \cline{2-6}  & **Task-Aware** & **Easy Translation (LLAMA-2)** & **0.914** & 0.839 & **0.884** & **1.037** & 10.849 \\  & & (LLAMA-3) & **0.917** & 0.808 & **0.881*** & **0.801*** & 10.401 \\  & & ( Tower-Restruct) & **0.885** & 0.883 & **0.878** & **1.277** & 11.137 \\  & & **CoGeit (Torre-Inspired)** & **0.903** & 0.871 & 0.875 & **2.432** & 2.024 \\ \hline
**Translatability-Aware** & **Selection** & **0.914** & **0.891* & **0.899*** & **2.096** & **1.830*** \\  & & **Time-tune (Basic)** & **0.912** & 0.848 & **0.886** & **2.123** & **1.932** \\  & & (MT) & **0.904** & 0.851 & 0.871 & **2.119** & **1.997** \\  & & (Reference) & **0.881** & 0.812 & 0.859 & **2.284** & 2.012 \\ \hline \end{tabular}
\end{table}
Table 9: Detailed results of English-Russian pair using different rewrite methods.





\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Label** & **Original** & **Rewrite** & **Original MT** & **Rewrite MT** & \(\mathbf{XCOMET}(s,t)\) & \(\mathbf{xCOMET}(s^{\prime},t^{\prime})\) \\ \hline
**Simplified** & When Michael “Hopper” & When Michael McGrath Als Michael “Hopper" & As Michael McGrath den 0.906 & 0.945 \\  & McCGraph _labeled_ as hall in, & **there** the ball hit, Mo-McGrath einen Ball & All die Lutt warf, & \\  & Molley kept highestened: for rifling a subline goal & key **jumped** highest and & Increkenwar, gargo Molyne and hochsen und schosen & \\  & to the roof of the net. & the top of the net. & time herkten Trefer & wunderschneen Trefer in \\  & Derry City _vorn_ the President’s & Derry City _vorn_ the president’s Cup tile by defeating Shamrock Rowsz 2-0. & Derry City _cay@187377_ & Derry City _cay@187377_ & 0.648 \\  & Derby City _vorn_ the president's & Derry City _vorn_ the president's Cup tile by defeating Shamrock Rowsz 2-0. & Derry City _cay@1873777_ & 0.648 \\  & Or simply _vornes_ over Sharrock Rowsz & der & DeShamrock Rowsz 2-0. & Derry City _cay@1873777_ & 0.648 \\ \hline
**Detailed** & The great majority of rankers never advanced beyond principals. & The vast majority of soldiers remained in the lowest ranked personicils. & Die groke Mehrheit der Reiter schaffle es mei über den Rang eines principalis. & Olehrewiegende 0.938 & 0.982 \\  & I’ve noticed you almost need line of sight for it to work. & It appears that visibility plays a crucial role in effectiveness of the process. & den Rang eines principalis. & high valamenet \\ \hline
**Fluence** & It’s a thing I’ve never said before either. & It appears that visibility plays a crucial role in the effectiveness of the process. & 31 mandetail, who just plan focuses basic mouth tree support impact. & 0.98 & 1.0 \\ \cline{2-5}  & When I started in summer with those multi-source experiments. & It appears that visibility plays a crucial role in the effectiveness of the process. & 32 mandetail, we only plan focus basic mouth tree support impact. & 0.98 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Percentage of occurrence (%) where the rewrite is a direct copy of the original source sentence.

\begin{table}
\begin{tabular}{l l r r r} \hline \hline
**Type** & **Prompt/Model** & **EN-DE** & **EN-RU** & **EN-ZH** \\ \hline
**MT-Agnostic** & **Simplification** (LLaMA-2) & 2.06 & 2.37 & 2.37 \\  & (LLaMA-3) & 0.39 & 0.33 & 0.29 \\  & (Tower-Instruct) & 28 & 29.3 & 30.2 \\  & **Paramphase** (LLaMA-2) & 0 & 0 & 0 \\  & (LLaMA-3) & 0.06 & 0.07 & 0.03 \\  & (Towe-Instruct) & 37.3 & 38.2 & 38 \\  & (DIPPER (L80/060)) & 0.19 & 0.94 & 1.04 \\  & (DIPPER (L80/040)) & 0.51 & 1.5 & 1.53 \\  & (DIPPER (L60/040)) & 1.48 & 2.5 & 2.44 \\  & **Stylistic** (CoEdit GEC) & 42.6 & 44 & 48.3 \\  & (CoEdit C Coherent) & 82.2 & 91.9 & 93.2 \\  & (CoEdit Understandable) & 1.61 & 1.88 & 1.53 \\  & (CoEdit Formal) & 5.33 & 3.76 & 5.5 \\ \hline
**Task-Aware** & **Easy Translation** (LLaMA-2) & 3.04 & 3.55 & 3.63 \\  & (LLaMA-3) & 0.24 & 0.66 & 0.27 \\  & ( Tower-Instruct) & 12.3 & 18.6 & 15.4 \\  & **CoT** (Tower-Instruct) & 0.71 & 1.45 & 1.53 \\ \hline
* \\  & (MT) & 3.73 & 3.42 &
- \\  & (Reference) & 6.17 & 7.85 &
- \\ \hline \hline \end{tabular}
\end{table}
Table 12: Examples of rewrites for each annotation label (**Simplified**, **Detailed** and **Fluency**).




\begin{table}
\begin{tabular}{l l l l l l l l c} \hline
**PromptModel** & **Original** & **Rewrite** & **Original MT** & **Rewrite MT** & **Flesch(\(s\))** & **Flesch(\(r^{\prime}\))** & **WSTF(\(t\))** & **WSTF(\(t^{\prime}\))** \\ \hline
**Simplification** (LAMA-3) & The standard & **Sle method**: We trained with the same model as in our experiments. In this paper, we use a different set of models to train the model on the training dataset and compare it with other methods. For example, we use a different set of models for each experiment. We also use a different set of models for each experiment.





\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Language** & **Prompt/Model** & \(\mathbf{xCOMET}(s,t)\) & \(\mathbf{xCOMET}(s,t,r)\) & \(\textbf{MetricX}(s,t)\) & \(\textbf{MetricX}(t,r)\) \\ \hline  & Original & 0.893 & 0.898 & 2.038 & 1.534 \\
**EN-DE** & Simplification (Ava-23 BB) & 0.901 & 0.900 & 1.956 & 1.624 \\  & Simplification (Tower-Instruct 13B) & **0.924** & **0.912** & **1.562** & **1.445** \\ \hline  & Original & 0.872 & 0.868 & 2.535 & 2.028 \\
**EN-RU** & Simplification (Ava-23 AB) & 0.880 & **0.875** & 2.428 & 1.938 \\  & Simplification (Tower-Instruct 13B) & **0.901** & **0.889** & **2.137** & **1.861** \\ \hline \hline \end{tabular}
\end{table}
Table 16: Results with two additional LLMs for rewriting: Aya-23 8B and Tower-Instruct 13B. Statistically significant average improvements (\(p\)-value \(<0.05\)) are **bold**. \(\texttt{xCOMET}(s,t)\): translatability (\(\uparrow\)); \(\texttt{xCOMET}(s,t,r)\): overall translation quality (\(\uparrow\)); MetricX\((s,t)\): quality estimation (\(\downarrow\)); MetricX\((t,r)\): reference-based metric (\(\downarrow\)).

\begin{table}
\begin{tabular}{c l l l l l l} \hline \hline
**Language** & **MT System** & **Prompt/Model** & \(\mathbf{xCOMET}(s,t)\) & \(\mathbf{xCOMET}(s,t,r)\) & \(\textbf{MetricX}(s,t)\) & \(\textbf{MetricX}(t,r)\) \\ \hline  & Tower-Instruct 7B & Original & 0.893 & 0.898 & 2.038 & 1.534 \\  & Tower-Instruct 7B & Simplification & **0.915** & **0.907** & 1.504 & 1.519 \\ \cline{2-7}  & Aya-23 8B & Original & 0.887 & 0.891 & 1.926 & 1.554 \\  & & Simplification & **0.911** & **0.902** & 1.660 & 1.571 \\  & Tower-Instruct 13B & Original & 0.880 & 0.887 & 2.043 & 1.522 \\  & Tower-Instruct 13B & Simplification & **0.900** & **0.893** & **1.778** & 1.556 \\ \hline  & Tower-Instruct 7B & Original & 0.872 & 0.868 & 2.535 & 2.028 \\  & Tower-Instruct 7B & Simplification & **0.921** & **0.891** & **1.135** & **1.921** \\ \cline{2-7}  & Ava-23 8B & Original & 0.863 & 0.852 & 2.711 & 2.323 \\  & Symplification & **0.892** & **0.872** & **2.300** & **2.173** \\ \cline{2-7}  & Tower-Instruct 13B & Original & 0.887 & 0.882 & 2.290 & 1.915 \\  & Significant & **0.894** & 0.875 & 2.296 & 1.915 \\ \hline  & Tower-Instruct 7B & Original & 0.786 & 0.794 & 3.445 & 2.282 \\  & Tower-Instruct 7B & Simplification & **0.821** & **0.802** & **1.521** & **2.227** \\ \cline{2-7}  & Ara-23 8B & Original & 0.769 & 0.779 & 3.758 & 2.572 \\  & Symplification & **0.793** & **0.788** & **3.433** & 2.530 \\ \cline{2-7}  & Tower-Instruct 13B & Original & 0.755 & 0.764 & 3.421 & 2.341 \\  & Simpleification & **0.772** & 0.767 & 3.236 & 2.413 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Results with two additional LLMs as MT system: Aya-23 8B and Tower-Instruct 13B. Simplification is done with Tower-Instruct 7b. Statistically significant average improvements (\(p\)-value \(<0.05\)) over their respective original baselines are **bold**. \(\texttt{xCOMET}(s,t)\): translatability (\(\uparrow\)); \(\texttt{xCOMET}(s,t,r)\): overall translation quality (\(\uparrow\)); MetricX\((s,t)\): quality estimation (\(\downarrow\)); MetricX\((t,r)\): reference-based metric (\(\downarrow\)).





\begin{table}
\begin{tabular}{c l c c c c} \hline \hline
**Language** & **Prompt/Model** & \(\mathbf{xCOMET}(s,t)\) & \(\mathbf{xCOMET}(s,t,r)\) & \(\mathbf{MetricX}(s,t)\) & \(\mathbf{MetricX}(t,r)\) \\ \hline  & Original & 0.866 & 0.755 & 2.437 & 4.033 \\
**CS-UK** & Simplification & **0.885** & 0.749 & 2.355 & 4.053 \\  & Selection & **0.930** & 0.748 & 3.050 & 4.053 \\ \hline  & Original & 0.969 & 0.622 & 1.869 & 4.760 \\
**DE-EN** & Simplification & **0.975** & **0.632** & 1.856 & 4.600 \\  & Selection & **0.979** & **0.631** & 1.856 & 4.599 \\ \hline  & Original & 0.582 & 0.556 & 8.057 & 5.758 \\
**HE-EN** & Simplification & 0.562 & 0.514 & 8.671 & 6.374 \\  & Selection & **0.639** & 0.514 & 9.192 & 6.541 \\ \hline  & Original & 0.884 & 0.841 & 3.473 & 2.688 \\
**JA-EN** & Simplification & 0.896 & 0.828 & 3.303 & 2.929 \\  & Selection & **0.918** & 0.827 & 3.659 & 2.964 \\ \hline  & Original & 0.938 & 0.921 & 3.024 & 1.823 \\
**RU-EN** & Simplification & 0.945 & 0.922 & 2.909 & 1.879 \\  & Selection & **0.954** & 0.923 & 3.079 & 1.912 \\ \hline  & Original & 0.934 & 0.929 & 2.959 & 1.507 \\
**UK-EN** & Simplification & **0.951** & 0.929 & 2.684 & 1.595 \\  & Selection & **0.962** & 0.929 & 3.055 & 1.656 \\ \hline  & Original & 0.797 & 0.524 & 5.099 & 5.666 \\
**ZH-EN** & Simplification & **0.809** & **0.530** & 4.849 & 5.582 \\  & Selection & **0.827** & 0.528 & 5.202 & 5.800 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Results with into-English and non-English language pairs. Simplification is done with Tower-Instruct 7B. Statistically significant average improvements (\(p\)-value \(<0.05\)) over their respective original baselines are **bold**. \(\text{xCOMET}(s,t)\): translatability (\(\uparrow\)); \(\text{xCOMET}(s,t,r)\): overall translation quality (\(\uparrow\)); \(\text{MetricX}(s,t)\): quality estimation (\(\downarrow\)); \(\text{MetricX}(t,r)\): reference-based metric (\(\downarrow\)).



In this survey, you will be asked questions about **20 pairs of sentences** written in German.

First, you will be asked to compare the two **sentences** to each other.

[Example]

**Sentence 1:** Das ist eine sehr schone Rohre. Das schicke Spitzenmuster ist weich und elegant.

**Sentence 2:** Das ist eine sehr schone Rohre. Sie ist stilvoll und weich.

Second, you will be given a reference sentence and asked to choose which of the two **sentences** better capture the meaning of the reference.

[Example]

**Reference:** Das ist ein sehr schoner Rock. Das Spitzenmuster ist stilvoll und weich.

**Sentence 1:** Das ist eine sehr schone Rohre. Das schicke Spitzenmuster ist wieich und elegant.

**Sentence 2:** Das ist eine sehr schone Rohre. Sie ist stilvoll und weich.

We estimate that the survey will take approximately **20 minutes** to complete.

[TABLE:A3.T5][ENDTABL=

Figure 6: Survey content of the first part to compare Original MT vs. Rewrite MT. To avoid position bias, we randomly shuffle the order of original translations (\(t\)) and translations of rewrites (\(t^{\prime}\)) for **Sentence 1** and **2**.

 

* Now let's compare our two sentences to a reference:

**Reference:**Es ist mit gespreizten Wetterbrettern verkleidet, die schmaler sind als die des Haupthauses.

**Sentence 1:**Er ist mit abgewinkelten Wetterschinden verkleidet, die schmaler sind als die des Haupthauses.

**Sentence 2:**Das Außere des kleineren Cottages ist mit schlanken, horizontalen Brettern bedeckt, die schmaler sind als die des großeren Hauptcottages.

\begin{tabular}{c c} Sentence 1 & Sentence 2 \\ Which one best captures the meaning of the & \\ reference? & \\ \end{tabular}

If you would like to further explain your answers, please do so here:

Figure 7: Survey content of the second part to compare to the **Reference translation**. An optional text box is given for each example for further comments.

 



**Sentence 1:** His tombstone represents him in armor, holding a shield with three cooking pots, marmites, on it.

**Sentence 2:** His tombstone shows him in armor, holding a shield with three cooking pots, marmites, on it.

To what extent does Sentence 2 capture the meaning of Sentence 1?

1: Not capture meaning

2: Partially

3: Mostly

4: Fully

If you would like to further explain your answers, please do so here:

Figure 8: Survey content to compare Original (**Sentence 1**) vs. Rewrite (**Sentence 2**).