# Prompting LLMs: Length Control for Isometric Machine Translation

Dávid Javorský, Ondřej Bojar, François Yvon

<figure><img src="image_1.png" /></figure>

## To cite this version:

Dávid Javorský, Ondřej Bojar, François Yvon. Prompting LLMs: Length Control for Isometric Machine Translation. 22nd International Conference on Spoken Language Translation (IWSLT 2025), Jul 2025, Vienne, Austria. pp.119-137, 10.18653/v1/2025.iwslt-1.11. http://05208907

HAL Id: hal-05208907

https://hal.science/hal-05208907v1

Submitted on 13 Aug 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
# Prompting LLMs: Length Control for Isometric Machine Translation

Dávid Javorský¹ and Ondřej Bojar¹ and François Yvon²

¹Charles University, Faculty of Mathematics and Physics, Prague, Czechia

²Sorbonne Université, CNRS, ISIR, Paris, France

{javorsky,bojar}@ufal.mff.cuni.cz francois.yvon@cnrs.fr

## Abstract

In this study, we explore the effectiveness of isometric machine translation across multiple language pairs (En→De, En→Fr, and En→Es) under the conditions of the IWSLT Isometric Shared Task 2022. Using eight open-source large language models (LLMs) of varying sizes, we investigate how different prompting strategies, varying numbers of few-shot examples, and demonstration selection influence translation quality and length control. We discover that the phrasing of instructions, when aligned with the properties of the provided demonstrations, plays a crucial role in controlling the output length. Our experiments show that LLMs tend to produce shorter translations only when presented with extreme examples, while isometric demonstrations often lead to the models disregarding length constraints. While few-shot prompting generally enhances translation quality, further improvements are marginal across 5, 10, and 20-shot settings. Finally, considering multiple outputs allows to notably improve overall tradeoff between the length and quality, yielding state-of-the-art performance for some language pairs.

## 1 Introduction

Accurate and concise translations are increasingly needed in media applications such as subtitling (Matusov et al., 2019; Karakanta et al., 2020) and dubbing (Federico et al., 2020; Lakew et al., 2021; Tam et al., 2022; Lakew et al., 2022; Rao et al., 2023), where length constraints are critical. Dubbing, in particular, requires translations to stay within ±10% of the source character-level length for seamless audio alignment (Lakew et al., 2022), a constraint known as *isometric machine translation*. The 2022 Isometric MT Shared Task (Anas-tasopoulos et al., 2022) found that most participating systems used lead tokens for length control, with some incorporating reranking or adjusted positional embeddings. Recent work also explored rein-

<figure><img src="image_3.png" /><figcaption>Figure 1: Overview of our experiment with prompts asking for different length constraints for the desired translation, complemented with few-shot examples demonstrating the given constraint (match) or not (no-match). Strong enough control to reach isometric translation needs matching instructions and preferably *Tiny* or *Short* demonstrations. The construction of demonstration sets is described in Section 3 and the prompt content is presented in Table 6 in Appendix B.2.</figcaption></figure>

forcement learning for isometric English-Hindi MT (Mhaskar et al., 2024) and examined length constraints in multiple language pairs (Bhavsar et al., 2022).

Controlling translation length remains challenging compared to other constrained MT tasks, such as politeness (Sennrich et al., 2016) or diversity (Shu et al., 2019) control. Previous approaches in encoder-decoder MT used length tokens (Lakew et al., 2019), positional embeddings (Takase and Okazaki, 2019; Buet and Yvon, 2021), restricted search spaces (Niehues, 2020), auxiliary length prediction tasks (Yang et al., 2020), and explicit compression methods (Li et al., 2020).

With the rise of large language models (LLMs) (Radford et al., 2019), there has been a shift toward prompting (Vilar et al., 2023; Zhang et al., 2023a; Bawden and Yvon, 2023) and fine-tuning (Zhang et al., 2023b; Moslem et al., 2023) for MT. Prompting strategies notably affect performance, especially in few-shot settings (Vilar et al., 2023). Studies found that randomly selected examples often improve results (Zhang et al., 2023a; Bawden and
<table><caption>Table 1: The average target-to-source sample length ratio and its standard deviation (LR), length compliance (LC), i.e. the percentage of target-side sentences within a ±10% range of the source character count, and the number of samples for two setups (*Development* and *Final Evaluation*) and for the testset (the MuST-C tst-COMMON and blind test sets) and the devset (MuST-C). The devset is used for selecting examples for few-shot prompting.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Setup</th><th colspan="3">En-De</th><th colspan="3">En-Fr</th><th colspan="3">En-Es</th></tr><tr><th>LR</th><th>LC↑</th><th>Count</th><th>LR</th><th>LC↑</th><th>Count</th><th>LR</th><th>LC↑</th><th>Count</th></tr></thead><tbody><tr><td>Dev</td><td>Both</td><td>1.14±0.3</td><td>38.2</td><td>1415</td><td>1.14±0.3</td><td>36.4</td><td>1412</td><td>1.08±0.3</td><td>50.5</td><td>1316</td></tr><tr><td rowspan="2">Test</td><td>Development</td><td>1.15±0.2</td><td>37.5</td><td>200</td><td>1.16±0.2</td><td>34.0</td><td>200</td><td>1.03±0.2</td><td>58.0</td><td>200</td></tr><tr><td>Final Evaluation</td><td>1.03±0.2</td><td>65.5</td><td>200</td><td>1.09±0.5</td><td>72.5</td><td>200</td><td>0.98±0.2</td><td>64.0</td><td>200</td></tr></tbody></table>

<table><caption>Table 2: Statistics of pools for En→De: The number of samples, minimum and maximum target/source length ratio, and its average and standard deviation.</caption><thead><tr><th rowspan="2">Pool type</th><th colspan="4">En-De</th></tr><tr><th>Count</th><th>Min</th><th>Max</th><th>avg±std</th></tr></thead><tbody><tr><td>Random</td><td>1415</td><td>0.43</td><td>5.80</td><td>1.14±0.27</td></tr><tr><td>Isometric</td><td>537</td><td>0.90</td><td>1.10</td><td>1.02±0.05</td></tr><tr><td>Same</td><td>50</td><td>0.99</td><td>1.00</td><td>1.00±0.00</td></tr><tr><td>Short</td><td>343</td><td>0.43</td><td>1.00</td><td>0.90±0.11</td></tr><tr><td>Tiny</td><td>50</td><td>0.43</td><td>0.81</td><td>0.68±0.11</td></tr></tbody></table>

Yvon, 2023), though performance gains plateau beyond five examples (Chowdhery et al., 2023; Vilar et al., 2023). While models like BLOOM tend to overgenerate in zero-shot settings (Bawden and Yvon, 2023), fine-tuning methods such as QLoRA (Zhang et al., 2023b) have shown superior performance over few-shot learning. Real-time adaptive MT has also demonstrated strong results, with models like ChatGPT rivaling traditional MT systems (Moslem et al., 2023; Hendy et al., 2023). The use of LLMs for MT has led to the exploration of various prompt templates, with simple structures like '[src]: [input] 
[tgt]:' proving effective (Zhang et al., 2023a; Briakou et al., 2023; Zeng et al., 2022). The impact of example selection has also been examined, confirming that beyond five-shot settings, improvements become marginal (Garcia et al., 2023; Zhang et al., 2023a; Chowdhery et al., 2023; Vilar et al., 2023).

Given these insights, we explore the application of LLMs to isometric MT, focusing on length control strategies. We analyze four prompting approaches: (1) uncontrolled translation, (2) isometric translation (±10% length variation), (3) same-length translation, and (4) shorter translation, each paired with corresponding demonstration sets. Experiments are conducted on eight open-weight models (Llama 3, Gemma 2, Qwen 2 of two sizes each, and Mistral and Mixtral) across 0, 5, 10, and 20-shot settings for En→De, En→Fr, and En→Es, following the 2022 Isometric Shared Task setup (Anastasopoulos et al., 2022).

Our results show that few-shot demonstrations affect translation outputs, but precise length control requires well-aligned instructions reflecting example properties, as summarized in Figure 1. Additionally, we show that generating multiple outputs with different example sets substantially improves length control, matching competitive isometric MT systems and offering high potential for synthetic data creation in training encoder-decoder models. We publicly release all collected data for potential future analyses.<fnref n="1" />

## 2 Experimental Setup

**Development** First, we conduct experiments with multiple settings (varying prompt type, the type of pools of demonstrations, and shot count in few-shot learning) to identify the best-performing configuration for length control. We refer to this as the *Development* setup and use the following data:

* **Demonstration set:** We use the MuST-C devset for selecting few-shot examples. We choose the devset over the trainset to reserve the latter for potential future fine-tuning.

* **Testset:** We use the first 200 examples from the MuST-C tst-COMMON, matching the number of examples in the evaluation blindset of the 2022 Isometric Shared Task.

**Final Evaluation** We then use the best-performing setting from the *Development* and evaluate it on the Isometric Shared Task test set:

* **Demonstration set:** We use the same demonstration set as in the *Development* setup.

* **Testset:** We use the blindset from the IWSLT 2022 Isometric Shared Task, which consists of dialogues extracted from YouTube videos, totaling 200 examples.²

<footnote n="1">¹https://github.com/J4VORSKY/Isometric-MT
²https://github.com/amazon-research/isometric-slt/tree/main/dataset</footnote>
The statistics of the datasets used in both steps
are displayed in Table 1.

**Metrics** Following the Isometric Shared Task, we use *BERTScore*<fnref n="3" /> (Zhang et al., 2020) to evaluate translation quality. For completeness, we also report *BLEU* (Papineni et al., 2002) scores using *sacreBLEU* (Post, 2018).⁴,⁵ We assess adherence to the ±10% length constraint using the *Length Compliance* (LC) metric (Anastasopoulos et al., 2022). Additionally, we report the average target-to-source *Length Ratio* in *Development* experiments and use it alongside Length Compliance in the *Final Evaluation* to gauge length control.

**Models** We use the Ollama library⁶ to load all models, which are provided in quantized versions (4-bit) without instruction fine-tuning (more details in Appendix B). Models used in our experiments include: llama3:8b, llama3:70b (Dubey et al., 2024); gemma2:9b, gemma2:27b (Gemma Team et al., 2024); qwen2:7b, qwen2:72b (Yang et al., 2024); mistral:7b (Jiang et al., 2023) and mixtral:8x7b (Jiang et al., 2024). For detailed descriptions, refer to the original papers.

## 3 Prompts

In our experiments, we use English as the language of the prompts (Zhang et al., 2023a) and explicitly specify the source and target languages within the prompt (Zhang et al., 2023a; Bawden and Yvon, 2023). Our focus is on length control when testing various prompt formulations. While large language models (LLMs) show strong performance in machine translation, they sometimes lag behind supervised neural models (Zhang et al., 2023a; Chowdhery et al., 2023; Kocmi et al., 2023). To our knowledge, length control has not been extensively explored for LLMs in machine translation.

**Prompt construction** We construct prompts by concatenating template parts and replacing placeholders with the appropriate values. The *Random* (uncontrolled) template instructs the model to generate a translation of the source sentence without any length restrictions. In the *Isometric* template, the model is instructed to generate a translation within ±10% of the source text's character count.

The *Same* template instructs the model to produce a translation that exactly matches the source text length, while the *Short / Tiny* template directs the model to generate a shorter translation, as the length ratios between studied language pairs often exceed 1, and a standard translation (typically longer) is not desired. A detailed overview of the prompt templates is in Table 6 in Appendix B.2.

We evaluate models in zero-shot and few-shot settings. They often overgenerate, adding explanations or extra translations, as noted by Bawden and Yvon (2023). While these authors used regular expressions to extract translations, we prevent this by explicitly instructing models to output only the translation, which proves effective. Further analysis is in Appendix A.

**Sample Selection** In preparing examples for the few-shot setting, we construct sampling pools by filtering the demonstration set based on the following criteria: Random selects samples without any filtering; Isometric contains only examples with a target-to-source length ratio within ±10%; Same sorts references by increasing $|r - 1.0|$ (where $r$ is the length ratio) and selects the top $N = 50$ instances; Short selects samples with target-to-source ratios in the range [0, 1]; Tiny samples the 50 examples with the smallest target-to-source ratio. The illustration is in Figure 1.

Statistics for each sampling pool for En→De are in Table 2. As other languages follow the same trend, their statistics are in Table 7 in Appendix B.3. Following Zhang et al. (2023a), we use the following template for in-context samples: `[src lang]: [src sentence] → [tgt lang]: [tgt sentence]`.

## 4 Analysis

In all experiments, the prompts remain identical across all models within a given setting. To reduce the bias of sampling from demonstration sets, we performed 10 runs for every setting.

### 4.1 Prompt and Pool Type Relation

First, we analyze how much the selection of examples is related to the instruction provided in the prompt in the few-shot prompting and how this combination influences the translation length. We therefore compare two setups:

**Prompt and Pool Type Match** We create matching pairs of prompts and pool types as follows:

<footnote n="3">³https://pypi.org/project/bert-score/0.3.11/
⁴https://github.com/mjpost/sacrebleu
⁵Signature: nrefs:1|case:mixed|eff:no|tok:13a|smooth:exp|version:2.4.2
⁶https://ollama.com/library</footnote>
<table><caption>Table 3: The evaluation is conducted as follows: We first compute the average target length per input sentence across 10 runs. Next, we calculate the target-to-source length ratio for each instance and average these values for each pool type. The results are reported separately for cases where the instructions match (‘Yes’) or do not match (‘No’) the sample properties in 5-shot prompting. Differences with a *p*-value < 0.1 for each pool type are underlined.</caption><thead><tr><th rowspan="2">Model \ Match</th><th colspan="2">Random</th><th colspan="2">Isometric</th><th colspan="2">Same</th><th colspan="2">Short</th><th colspan="2">Tiny</th></tr><tr><th>No</th><th>Yes</th><th>No</th><th>Yes</th><th>No</th><th>Yes</th><th>No</th><th>Yes</th><th>No</th><th>Yes</th></tr></thead><tbody><tr><td>gemma2:27b</td><td>1.100</td><td>1.097</td><td>1.099</td><td>1.094</td><td>1.098</td><td>1.097</td><td><u>1.087</u></td><td><u>1.011</u></td><td><u>1.066</u></td><td><u>0.955</u></td></tr><tr><td>gemma2:9b</td><td>1.108</td><td>1.106</td><td>1.106</td><td>1.101</td><td><u>1.106</u></td><td><u>1.073</u></td><td><u>1.099</u></td><td><u>1.026</u></td><td><u>1.080</u></td><td><u>0.981</u></td></tr><tr><td>llama3:70b</td><td>1.149</td><td>1.151</td><td>1.149</td><td>1.139</td><td>1.141</td><td>1.134</td><td><u>1.138</u></td><td><u>1.005</u></td><td><u>1.122</u></td><td><u>0.905</u></td></tr><tr><td>llama3:8b</td><td>1.106</td><td>1.100</td><td>1.093</td><td>1.108</td><td>1.099</td><td>1.112</td><td><u>1.085</u></td><td><u>1.048</u></td><td><u>1.056</u></td><td><u>0.994</u></td></tr><tr><td>mistral:7b</td><td>1.133</td><td>1.129</td><td>1.126</td><td>1.128</td><td>1.135</td><td>1.125</td><td>1.121</td><td>1.105</td><td><u>1.138</u></td><td><u>1.085</u></td></tr><tr><td>mixtral:8x7b</td><td>1.402</td><td>1.411</td><td>1.375</td><td>1.362</td><td>1.378</td><td>1.381</td><td>1.385</td><td>1.297</td><td>1.363</td><td>1.265</td></tr><tr><td>qwen2:72b</td><td>1.223</td><td>1.169</td><td>1.195</td><td>1.178</td><td>1.184</td><td>1.173</td><td>1.170</td><td>1.128</td><td>1.164</td><td>1.129</td></tr><tr><td>qwen2:7b</td><td>1.132</td><td>1.160</td><td>1.144</td><td>1.125</td><td>1.129</td><td>1.129</td><td>1.128</td><td>1.135</td><td>1.117</td><td>1.095</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>Figure 2: The percentage of input sentences (across all language directions) for which at least one of ten generated translations meets the isometric condition when the model is prompted to produce isometric, same-length, short, and tiny outputs aligned with respective 5-shot demonstration sets. This evaluation is restricted to input sentences where the particular model did not generate any isometric translation in ten attempts using the uncontrolled prompt.</figcaption></figure>

Random-Random, Isometric-Isometric, Same-Same, Short-Short, Tiny-Tiny.

**Prompt and Pool Type Mismatch** We keep the Random prompt for all pool types.

We compare these two configurations for En→De in Table 3, the remaining translation directions are documented in Appendix C. Our results indicate that the length ratios are mostly affected when the instruction aligns with the pool type, compared to when there is no such match (we can also see a tendency to generate shorter outputs when

comparing “no alignment” columns across different pool types, but the difference is negligible). This match-versus-no-match difference is statistically significant in Gemma and Llama models, particularly for the Short and Tiny pools. Additionally, the Isometric and Same pools do not appear to induce shorter translations compared to random sampling, as evidenced by the similar values observed in the first three columns. We hypothesize that requesting outputs to preserve the input length somehow guides models to reproduce the distribution of the training data rather than actually considering the length (i.e. models implicitly assume that typical translation is of the same length). However, in studied language directions, what is considered as normal ratio, is skewed towards values greater than 1. In other words, models naturally follow the length distribution they were trained on and can overcome this bias only when extreme examples are provided.

To further highlight the utility of our approach, Figure 2 focuses on cases where models consistently fail to produce isometric translations under the Random-Random setting, even after 10 runs. This occurs in about 30% of devset sentences on average. The figure shows how alternative prompts improve length compliance, with Tiny and Short settings achieving up to 80% isometric translations for Llama3:70b when at least one of 10 runs succeeds. The overall practical ability of each of the models to achieve isometric translation is summarized by the two numbers above the bars in Figure 2. The first number indicates the percentage of devset sentences that were translated in a compliant way by default and the second number indicates to which proportion we raised this using the Tiny prompt. Note that in the worst case, this level of
<figure><img src="image_5.png" /><figcaption>Figure 3: En→De translation quality (BERTScore, dashed lines and the right hand y-axes) and length ratio (solid lines and left-hand y-axes) for all few-shot settings, models and language pairs.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 4: En→De trade-off between the length ratio (x-axis) and translation quality (y-axis) for 5, 10, 20-shot settings and all models.</figcaption></figure>

compliance is reached at 20x the translation cost (10 attempts by default plus 10 *Tiny* attempts). In practice, however, we can switch to the *Tiny* prompt after the first unsuccessful attempt in the default generation. The number of additional generations with *Tiny* setting depends on resource constraints and requirements. But even after just one attempt, *Llama3:70b* achieves isometric translations in 35% of cases. Full results are in Appendix C.

## 4.2 Comparing Demonstration Pools

We give below a more detailed evaluation across all few-shot settings, models, and pool types, using only settings when the instruction matches the pool type and where the model is instructed to output only the translation. Both length ratios and

BERTScore values are reported for En→De, with the results presented in Figure 3. For a comprehensive view of all few-shot settings, detailed numerical results are reported in Appendix D.

**Length Ratios and Length Control** In terms of length ratio, all models consistently exhibit the same trend: the ratios are highest for random sampling, followed by isometric sampling, and then by shorter examples. Providing extreme examples encourages models to produce shorter translations. Interestingly, in the zero-shot setting, we observe a length ratio lower than 1.0 for the Llama and Gemma models. However, when demonstrations are also given in few-shot settings for these models, translations are longer, even when the associated
<table><caption>Table 4: *Final Evaluation* — Length Ratio (LR), Length Compliance (LC), BERTScore (BS) and BLEU — of the best setting (10-shot, pool type Tiny) across different Llama and Gemma models compared to the submissions of IWSLT Isometric Shared Task. The *k* values indicate the number of demonstration sampling runs (i.e. different outputs) from which we select the best one using COMETKIWI. To avoid any possible evaluation difference, we (re-)evaluated all the outputs, ours and IWSLT22 ones, using the script provided by the organizers of the shared task. The best results are in bold.</caption><thead><tr><th rowspan="2">System</th><th colspan="4">En→De</th><th colspan="4">En→Fr</th><th colspan="4">En→Es</th></tr><tr><th>LR↓</th><th>LC↑</th><th>BS↑</th><th>BLEU↑</th><th>LR↓</th><th>LC↑</th><th>BS↑</th><th>BLEU↑</th><th>LR↓</th><th>LC↑</th><th>BS↑</th><th>BLEU↑</th></tr></thead><tbody><tr><td>STRONGBASELINE</td><td><b>1.03</b></td><td>68.0</td><td>77.44</td><td>21.6</td><td>1.02</td><td>75.5</td><td><b>81.75</b></td><td><b>36.2</b></td><td><b>1.00</b></td><td>80.5</td><td>81.86</td><td>36</td></tr><tr><td>APPTEK-Constrained</td><td>1.11</td><td>86.5</td><td>77.32</td><td>18.7</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>NUV-Unconstrained</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.10</td><td>47.5</td><td>79.96</td><td>27.1</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>HW-TSC-Unconstrained</td><td><b>1.03</b></td><td>96.5</td><td>75.79</td><td>20.2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>HW-TSC-Constrained</td><td>1.28</td><td><b>98.0</b></td><td>74.07</td><td>17.9</td><td>1.19</td><td><b>96.0</b></td><td>76.11</td><td>31.5</td><td>1.18</td><td><b>96.5</b></td><td>78.57</td><td>29.9</td></tr><tr><td>APV-Unconstrained</td><td>1.68</td><td>39.0</td><td>73.68</td><td>16.5</td><td>1.21</td><td>45.0</td><td>77.77</td><td>32.9</td><td>1.05</td><td>49.5</td><td>80.87</td><td>35.3</td></tr><tr><td>WEAKBASELINE</td><td>1.29</td><td>43.0</td><td>74.86</td><td>15.5</td><td>1.48</td><td>37.0</td><td>77.18</td><td>25.2</td><td>1.38</td><td>51.0</td><td>78.32</td><td>27.7</td></tr><tr><td>model=gemma2:27b-k=1</td><td>1.07</td><td>43.5</td><td>77.08</td><td>19.0</td><td>1.05</td><td>47.5</td><td>78.30</td><td>32.7</td><td><b>1.00</b></td><td>55.5</td><td>83.20</td><td>40.3</td></tr><tr><td>model=gemma2:27b-k=3</td><td>1.08</td><td>58.0</td><td>77.96</td><td>20.2</td><td>1.07</td><td>60.5</td><td>79.96</td><td>33.5</td><td>1.01</td><td>66.5</td><td>83.29</td><td>40.3</td></tr><tr><td>model=gemma2:27b-k=5</td><td>1.09</td><td>62.5</td><td><b>77.98</b></td><td>20.4</td><td>1.06</td><td>62.5</td><td>80.01</td><td>33.9</td><td>1.02</td><td>68.0</td><td>83.16</td><td>40.0</td></tr><tr><td>model=gemma2:27b-k=10</td><td>1.08</td><td>68.5</td><td>77.84</td><td><b>21.9</b></td><td>1.08</td><td>69.0</td><td>80.05</td><td>35.6</td><td>1.01</td><td>70.5</td><td><b>83.62</b></td><td><b>40.8</b></td></tr><tr><td>model=gemma2:9b-k=1</td><td>2.24</td><td>42.5</td><td>77.04</td><td>17.7</td><td>0.00</td><td>0.0</td><td>0.00</td><td>0.0</td><td>1.02</td><td>54.5</td><td>82.47</td><td>39.0</td></tr><tr><td>model=gemma2:9b-k=3</td><td>1.19</td><td>58.5</td><td>77.24</td><td>20.6</td><td>1.07</td><td>60.5</td><td>80.38</td><td>34.1</td><td>1.03</td><td>65.5</td><td>83.41</td><td>36.8</td></tr><tr><td>model=gemma2:9b-k=5</td><td>1.07</td><td>64.5</td><td>77.38</td><td>20.9</td><td>1.06</td><td>65.5</td><td>80.66</td><td>35.5</td><td>1.03</td><td>73.0</td><td>83.30</td><td>37.2</td></tr><tr><td>model=gemma2:9b-k=10</td><td>1.08</td><td>64.0</td><td>77.48</td><td>21.7</td><td>1.06</td><td>70.5</td><td>80.72</td><td>34.9</td><td>1.03</td><td>73.0</td><td>83.17</td><td>37.6</td></tr><tr><td>model=llama3:70b-k=1</td><td>1.09</td><td>49.0</td><td>76.57</td><td>20.9</td><td>1.05</td><td>41.0</td><td>76.44</td><td>28.6</td><td>0.96</td><td>46.5</td><td>79.29</td><td>31.4</td></tr><tr><td>model=llama3:70b-k=3</td><td>1.06</td><td>62.5</td><td>77.18</td><td>22.1</td><td><b>1.00</b></td><td>55.5</td><td>77.64</td><td>30.9</td><td>1.03</td><td>59.5</td><td>80.64</td><td>34.4</td></tr><tr><td>model=llama3:70b-k=5</td><td>1.06</td><td>65.0</td><td>77.24</td><td>22.2</td><td>1.02</td><td>64.0</td><td>77.62</td><td>32.5</td><td>1.02</td><td>65.5</td><td>80.96</td><td>35.1</td></tr><tr><td>model=llama3:70b-k=10</td><td>1.07</td><td>69.0</td><td>77.23</td><td>21.7</td><td>1.04</td><td>68.0</td><td>78.24</td><td>33.7</td><td>1.02</td><td>70.5</td><td>81.37</td><td>35.8</td></tr><tr><td>model=llama3:8b-k=1</td><td>1.21</td><td>42.0</td><td>74.30</td><td>13.8</td><td>1.16</td><td>47.5</td><td>74.71</td><td>22.9</td><td>1.03</td><td>48.5</td><td>77.80</td><td>28.6</td></tr><tr><td>model=llama3:8b-k=3</td><td>1.09</td><td>56.0</td><td>75.79</td><td>15.9</td><td>1.09</td><td>60.5</td><td>75.96</td><td>25.5</td><td>0.99</td><td>65.0</td><td>79.76</td><td>30.6</td></tr><tr><td>model=llama3:8b-k=5</td><td>1.09</td><td>60.5</td><td>76.10</td><td>16.7</td><td>1.10</td><td>69.5</td><td>76.32</td><td>26.1</td><td>1.01</td><td>69.5</td><td>80.15</td><td>31.4</td></tr><tr><td>model=llama3:8b-k=10</td><td>1.09</td><td>65.0</td><td>76.28</td><td>16.9</td><td>1.08</td><td>75.0</td><td>77.02</td><td>26.2</td><td>1.03</td><td>74.0</td><td>79.88</td><td>29.0</td></tr><tr><td>OracleBLEU</td><td>1.04</td><td>78.0</td><td>80.82</td><td>37.6</td><td>1.03</td><td>85.0</td><td>83.93</td><td>52.9</td><td>1.01</td><td>88.5</td><td>87.01</td><td>57.5</td></tr></tbody></table>

demonstrations are short or very short.

**Few-shot Prompting** Another notable observation is that increasing the number of examples in few-shot prompting does not substantially enhance regular translation quality (i.e., translation without length restrictions), which is consistent with previous findings (Bawden and Yvon, 2023; Zhang et al., 2023a; Chowdhery et al., 2023). Including shorter examples sometimes improves adherence to length limitations (e.g., for llama3:8b); this effect is not observed for all models (e.g., for gemma2:27b).

**Translation Quality Scores** The largest translation quality scores are observed when the unfiltered pool (Random) is used, which is expected as this corresponds to an unconstrained setting. The top-performing model in terms of BERTScore for English-German translations is llama3:70b. For the other language pairs, gemma2:9b, gemma2:27b and qwen2:72b achieve the largest translation score.

**Length Ratio and Translation Quality Tradeoff**

We also compare translation scores with length ratios. The results are presented in Figure 4 for

En→De direction (the rest in Figure 7 in Appendix E). We can see that only Llama and Gemma models are capable of reaching 1.0 length ratio. Our results also highlight the impact of model size on performance, with larger models consistently outperforming their smaller counterparts in BERTScore, except for gemma2:9b which reports similar performance to its large counterpart.

**5 Final Evaluation**

Since Llama and Gemma models achieve the best performance on all language pairs on average, we select and evaluate them in *Final Evaluation* using the Isometric Shared Task blind set. We generate outputs using 10 distinct sets of 10 examples (10-shot), each drawn from the Tiny pool as it yields the best length control. We keep only outputs in the ±10% length constraint<fnref n="7" /> and select the best one according to reference-free COMET, i.e. COMETKIWI<fnref n="8" /> score (Rei et al., 2022). We then

<footnote n="7">⁷If none of the translations adhere to the ±10% length constraint, we keep the original unfiltered set.</footnote>

<footnote n="8">⁸https://huggingface.co/Unbabel/wmt22-cometkiwi-da</footnote>
compare these results with the submissions from the IWSLT 2022 Isometric Shared Task, specifically those from the APPTEK (Wilken and Matusov, 2022), HW-TSC (Li et al., 2022), Amazon Prime Video (APV), and NUV teams (Bhatnagar et al., 2022), in addition to the two (strong and weak) baselines provided by the organizers. For a brief overview of each system, please refer to Anas-tasopoulos et al. (2022). Additionally, we compare our results to an OracleBLEU setting, where the best translation is selected according to the sentence BLEU score across all configurations after filtering out translations that fall outside ±10% of the source character count. The results are summarized in Table 4.

Our results show that for the En→De and En→Es language pairs, the Gemma models achieve output quality comparable to the strong baseline. While the translation quality metrics surpass that of the strong baseline, the length control is slightly less precise. For En→Fr, however, the strong baseline continues to outperform our models in terms of quality as well as LR. Although generating 10 different outputs for each source sentence may not be feasible in practice, this approach could be beneficial for producing synthetic data for training isometric machine translation models.

# 6 Conclusion

In this paper, we explored the use of LLMs for isometric machine translation, focusing on strategies to control the translation length. Our key findings are as follows: First, effective length control in few-shot prompting requires the simultaneous use of appropriate demonstrations and matching instructions. Second, generating multiple outputs achieves the best trade-off between length control and translation quality, indicating the high capability of LLMs to generate desired outputs. It might be also useful for creating synthetic training data. Although prompting 10 times may seem inefficient, it would not be necessary for every sample in practice. Since half of the samples are already length-compliant — even with the uncontrolled *Random* prompt — compliance for the rest can be achieved iteratively by generating translations until the length constraint is met. Future work might benefit from fine-tuning LLMs or from a more in-depth analysis of the internal representation of length in LLMs to avoid many samples to generate.

# Limitations

We compare our results primarily with system submissions from the Isometric Shared Task 2022, as more recent models either do not address the language pairs examined in this study (e.g., Hindi-English by Mhaskar et al. (2024)) or are not publicly available (Bhavsar et al., 2022). Additionally, we do not evaluate performance on any downstream tasks, such as subtitling or dubbing.

We did not conduct a detailed analysis of ensemble methods, particularly concerning ensembling across different models or pool types. Moreover, for the Tiny and Same pools, we do not analyze the effect of varying *N*.

When collecting 10 outputs in *Final Evaluation*, the associated computational cost increases considerably. While this approach may not be feasible for real-world applications, it can be valuable for generating high-quality examples for isometric machine translation model training. To further reduce computational costs, one could regenerate only those translations that do not meet the specified length constraints.

Finally, it is important to note that we exclusively used quantized versions of the models in our experiments, likely resulting in sub-optimal translation scores.

# Acknowledgements

The work has been partially supported by the grant 272323 of the Grant Agency of Charles University, SVV project number 260 821 and by the grant CZ.02.01.01/00/23_020/0008518 (“Jazykověda, umělá inteligence a jazykové a řečové technologie: od výzkumu k aplikacím”).

# References

Antonios Anastasopoulos, Loïc Barrault, Luisa Benvogli, Marcely Zanon Boito, Ondřej Bojar, Roldano Cattoni, Anna Currey, Georgiana Dinu, Kevin Duh, Maha Elbayad, Clara Emmanuel, Yannick Estève, Marcello Federico, Christian Federmann, Souhir Gahbiche, Hongyu Gong, Roman Grundkiewicz, Barry Haddow, Benjamin Hsu, Dávid Javorský, Věra Kloudová, Surafel Lakew, Xutai Ma, Prashant Mathur, Paul McNamee, Kenton Murray, Maria Nádejde, Satoshi Nakamura, Matteo Negri, Jan Niehues, Xing Niu, John Ortega, Juan Pino, Elizabeth Salesky, Jiatong Shi, Matthias Sperber, Sebastian Stüker, Katsuhito Sudoh, Marco Turchi, Yوغesh Virkar, Alexander Waibel, Changhan Wang, and Shinji Watanabe. 2022. Findings of the IWSLT 2022 evaluation campaign. In *Proceedings of the*
19th International Conference on Spoken Language
Translation (IWSLT 2022), pages 98–157, Dublin,
Ireland (in-person and online). Association for Com-
putational Linguistics.

Rachel Bawden and François Yvon. 2023. Investigating the translation performance of a large multilingual language model: the case of BLOOM. In *Proceedings of the 24th Annual Conference of the European Association for Machine Translation*, pages 157–170, Tampere, Finland. European Association for Machine Translation.

Aakash Bhatnagar, Nidhir Bhavsar, Muskaan Singh, and Petr Motlicek. 2022. Hierarchical multi-task learning framework for isometric-speech language translation. In *Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)*, pages 379–385, Dublin, Ireland (in-person and online). Association for Computational Linguistics.

Nidhir Bhavsar, Aakash Bhatnagar, and Muskaan Singh. 2022. HMIST: Hierarchical multilingual isometric speech translation using multi-task learning framework and its influence on automatic dubbing. In *Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation*, pages 554–563, Manila, Philippines. Association for Computational Linguistics.

Eleftheria Briakou, Colin Cherry, and George Foster. 2023. Searching for needles in a haystack: On the role of incidental bilingualism in PaLM's translation capability. In *Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*, pages 9432–9452, Toronto, Canada. Association for Computational Linguistics.

François Buet and François Yvon. 2021. Toward Genre Adapted Closed Captioning. In *Proc. Interspeech 2021*, pages 4403–4407.

Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2023. PALM: Scaling language modeling with pathways. *Journal of Machine Learning Research*, 24(240):1–113.

Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783.

Marcello Federico, Robert Enyedi, Roberto Barra-
Chicote, Ritwik Giri, Umut Isik, Arvindh Krish-
naswamy, and Hassan Sawaf. 2020. From speech-to-
speech translation to automatic dubbing. In *Proceed-
ings of the 17th International Conference on Spoken
Language Translation*, pages 257–264, Online. Asso-
ciation for Computational Linguistics.

Xavier Garcia, Yamini Bansal, Colin Cherry, George Foster, Maxim Krikun, Melvin Johnson, and Orhan

Firat. 2023. The unreasonable effectiveness of few-
shot learning for machine translation. In Proceedings
of the 40th International Conference on Machine
Learning, ICML'23. JMLR.org.

Gemma Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al. 2024. Gemma 2: Improving open language models at a practical size. *arXiv preprint arXiv:2408.00118*.

Amr Hendy, Mohamed Abdelrehim, Amr Sharaf, Vikas Raunak, Mohamed Gabr, Hitokazu Matushita, Young Jin Kim, Mohamed Afify, and Hany Hassan Awadalla. 2023. How good are GPT models at machine translation? a comprehensive evaluation. *arXiv preprint arXiv:2302.09210*.

Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023. Mistral 7b. *arXiv preprint arXiv:2310.06825*.

Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al. 2024. Mixtral of experts. *arXiv preprint arXiv:2401.04088*.

Alina Karakanta, Matteo Negri, and Marco Turchi. 2020. Is 42 the answer to everything in subtitling-oriented speech translation? In *Proceedings of the 17th International Conference on Spoken Language Translation*, pages 209–219, Online. Association for Computational Linguistics.

Tom Kocmi, Eleftherios Avramidis, Rachel Bawden, Ondřej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Markus Freitag, Thamme Gowda, Roman Grundkiewicz, Barry Haddow, Philipp Koehn, Benjamin Marie, Christof Monz, Makoto Morishita, Kenton Murray, Makoto Nagata, Toshiaki Nakazawa, Martin Popel, Maja Popović, and Mariya Shmatova. 2023. Findings of the 2023 conference on machine translation (WMT23): LLMs are here but not quite there yet. In *Proceedings of the Eighth Conference on Machine Translation*, pages 1–42, Singapore. Association for Computational Linguistics.

Surafel M. Lakew, Marcello Federico, Yue Wang, Cuong Hoang, Yogesh Virkar, Roberto Barra-Chicote, and Robert Enyedi. 2021. Machine translation verbosity control for automatic dubbing. In *ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, pages 7538–7542.

Surafel M Lakew, Yogesh Virkar, Prashant Mathur, and Marcello Federico. 2022. Isometric MT: Neural machine translation for automatic dubbing. In *ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, pages 6242–6246. IEEE.
Surafel Melaku Lakew, Mattia Di Gangi, and Marcello Federico. 2019. Controlling the output length of neural machine translation. In *Proceedings of the 16th International Conference on Spoken Language Translation*, Hong Kong. Association for Computational Linguistics.

Zongyao Li, Jiaxin Guo, Daimeng Wei, Hengchao Shang, Minghan Wang, Ting Zhu, Zhanglin Wu, Zhengzhe Yu, Xiaoyu Chen, Lizhi Lei, Hao Yang, and Ying Qin. 2022. HW-TSC's participation in the IWSLT 2022 isometric spoken language translation. In *Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)*, pages 361–368, Dublin, Ireland (in-person and online). Association for Computational Linguistics.

Zuchao Li, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Zhuosheng Zhang, and Hai Zhao. 2020. Explicit sentence compression for neural machine translation. In *Proceedings of the AAAI Conference on Artificial Intelligence*, volume 34, pages 8311–8318.

Evgeny Matusov, Patrick Wilken, and Yota Georgakopoulou. 2019. Customizing neural machine translation for subtitling. In *Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)*, pages 82–93, Florence, Italy. Association for Computational Linguistics.

Shivam Ratnakant Mhaskar, Nirmesh J Shah, Mohammadi Zaki, Ashishkumar P Gudmalwar, Pankaj Wasnik, and Rajiv Ratn Shah. 2024. Isometric neural machine translation using phoneme count ratio reward-based reinforcement learning. *arXiv preprint arXiv:2403.15469*.

Yasmin Moslem, Rejwanul Haque, John D. Kelleher, and Andy Way. 2023. Adaptive machine translation with large language models. In *Proceedings of the 24th Annual Conference of the European Association for Machine Translation*, pages 227–237, Tampere, Finland. European Association for Machine Translation.

Jan Niehues. 2020. Machine translation with unsupervised length-constraints. In *Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)*, pages 21–35, Virtual. Association for Machine Translation in the Americas.

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In *Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics*, pages 311–318, Philadelphia, Pennsylvania, USA. Association for Computational Linguistics.

Matt Post. 2018. A call for clarity in reporting BLEU scores. In *Proceedings of the Third Conference on Machine Translation: Research Papers*, pages 186–191, Brussels, Belgium. Association for Computational Linguistics.

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. *OpenAI blog*, 1(8):9.

Zhiqiang Rao, Hengchao Shang, Jinlong Yang, Daimeng Wei, Zongyao Li, Jiaxin Guo, Shaojun Li, Zhengzhe Yu, Zhanglin Wu, Yuhao Xie, Bin Wei, Ji-awei Zheng, Lizhi Lei, and Hao Yang. 2023. Length-aware NMT and adaptive duration for automatic dubbing. In *Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)*, pages 138–143, Toronto, Canada (in-person and online). Association for Computational Linguistics.

Ricardo Rei, Marcos Treviso, Nuno M. Guerreiro, Chrysoula Zerva, Ana C Farinha, Christine Maroti, José G. C. de Souza, Taisiya Glushkova, Duarte Alves, Luisa Coheur, Alon Lavie, and André F. T. Martins. 2022. CometKiwi: IST-unbelab 2022 submission for the quality estimation shared task. In *Proceedings of the Seventh Conference on Machine Translation (WMT)*, pages 634–645, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.

Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Controlling politeness in neural machine translation via side constraints. In *Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*, pages 35–40, San Diego, California. Association for Computational Linguistics.

Raphael Shu, Hideki Nakayama, and Kyunghyun Cho. 2019. Generating diverse translations with sentence codes. In *Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*, pages 1823–1827, Florence, Italy. Association for Computational Linguistics.

Sho Takase and Naoaki Okazaki. 2019. Positional encoding to control output sequence length. In *Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*, pages 3999–4004, Minneapolis, Minnesota. Association for Computational Linguistics.

Derek Tam, Surafel M. Lakew, Yogesh Virkar, Prashant Mathur, and Marcello Federico. 2022. Isochrony-Aware Neural Machine Translation for Automatic Dubbing. In *Proc. Interspeech 2022*, pages 1776–1780.

David Vilar, Markus Freitag, Colin Cherry, Jiaming Luo, Viresh Ratnakar, and George Foster. 2023. Prompting PaLM for translation: Assessing strategies and performance. In *Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*, pages 15406–15427, Toronto, Canada. Association for Computational Linguistics.
Patrick Wilken and Evgeny Matusov. 2022. AppTek's submission to the IWSLT 2022 isometric spoken language translation task. In *Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)*, pages 369–378, Dublin, Ireland (in-person and online). Association for Computational Linguistics.

An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, et al. 2024. Qwen2 technical report. *arXiv preprint arXiv:2407.10671*.

Zijian Yang, Yingbo Gao, Weiyue Wang, and Hermann Ney. 2020. Predicting and using target length in neural machine translation. In *Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing*, pages 389–395, Suzhou, China. Association for Computational Linguistics.

Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, et al. 2022. Glm-130b: An open bilingual pre-trained model. In *The Eleventh International Conference on Learning Representations*.

Biao Zhang, Barry Haddow, and Alexandra Birch. 2023a. Prompting large language model for machine translation: A case study. In *International Conference on Machine Learning*, pages 41092–41110. PMLR.

Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020. BERTScore: Evaluating Text Generation with BERT. In *International Conference on Learning Representations*.

Xuan Zhang, Navid Rajabi, Kevin Duh, and Philipp Koehn. 2023b. Machine translation with large language models: Prompting, few-shot learning, and fine-tuning with QLoRA. In *Proceedings of the Eighth Conference on Machine Translation*, pages 468–481, Singapore. Association for Computational Linguistics.

## A Overgeneration

Bawden and Yvon (2023) have demonstrated that the BLOOM model tends to overgenerate, specifically it continues to produce translations in additional languages beyond the desired output. In our preliminary experiments, we observed similar behavior across several models, which manifested in two distinct ways: (1) models frequently provided explanations alongside the translation, and (2) models embedded the translation within a broader text.

To mitigate the issue of overgeneration, we implemented a straightforward yet highly effective solution. Specifically, we appended an instruction to the prompt, explicitly directing the model to

<figure><img src="image_7.png" /><figcaption>Figure 5: Restricted vs unrestricted prompt for 5-shot examples and the random pool when we discard everything after the first new line. In restricted, we add 'output translation only' at the end of the prompt. The red dashed line corresponds to a ratio of 1.0.</figcaption></figure>

output only the translation. This approach proved to be remarkably effective, obviating the need for more complex techniques such as truncation or the application of regular expressions to filter the translation. We evaluated the impact of this method on translation length within a 5-shot setting, utilizing a randomly selected pool with uncontrolled instruction types. For each model, we constructed 10 distinct prompts with different examples, and we discarded generated text after the first new line character because at this place an explanation often begins. The averaged length ratios are presented in Figure 5.

The results indicate that our approach maintains length consistency across all models and language pairs, with values remaining close to 1.0. The only
<table><caption>Table 5: The average percentage of translations that contain a new line, indicating overgeneration (5-shot setting). ‘Yes’ and ‘No’ columns denote the restricted and unrestricted prompt, respectively.</caption><thead><tr><th rowspan="2">Model</th><th colspan="2">En-De</th><th colspan="2">En-Fr</th><th colspan="2">En-Es</th></tr><tr><th>No</th><th>Yes</th><th>No</th><th>Yes</th><th>No</th><th>Yes</th></tr></thead><tbody><tr><td>gemma2:9b</td><td>100</td><td>0</td><td>100</td><td>2</td><td>100</td><td>2</td></tr><tr><td>gemma2:27b</td><td>100</td><td>1</td><td>100</td><td>2</td><td>100</td><td>1</td></tr><tr><td>llama3:8b</td><td>100</td><td>3</td><td>100</td><td>3</td><td>100</td><td>1</td></tr><tr><td>llama3:70b</td><td>100</td><td>0</td><td>99</td><td>0</td><td>99</td><td>0</td></tr><tr><td>qwen2:7b</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>qwen2:72b</td><td>12</td><td>3</td><td>7</td><td>1</td><td>5</td><td>1</td></tr><tr><td>mistral:7b</td><td>16</td><td>3</td><td>9</td><td>1</td><td>19</td><td>1</td></tr><tr><td>mixtral:8x7b</td><td>38</td><td>15</td><td>29</td><td>15</td><td>36</td><td>20</td></tr></tbody></table>

exceptions are mixtral:8x7b, which tends to generate longer text even with the restrictive instruction, and qwen2:7b, which is the only one that does not tend to overgenerate in the first place.

Since overgeneration, when it does occur, typically manifests itself as additional text generated after a newline character, we counted the occurrences of text generation following a newline in both the restricted and unrestricted settings to further evaluate the effectiveness of our method. As shown in Table 5, in many instances — particularly with the Llama and Gemma models — there is a clear tendency for models to generate explanatory text after a newline when output is unrestricted. Conversely, when the output is restricted to translation only, the occurrence of additional text is substantially reduced. Based on these observations, we adopted this restrictive instruction in all subsequent experiments and we also ignore any output after the newline character.

**Examples** Examples of overgeneration where (1) models frequently provided explanations alongside the translation, and (2) models embedded the translation within a broader text:

1. qwen2:72b: ... English: Not surprisingly, this destruction also endangers bonobo survival. ⊢ German: Überraschenderweise gefährdet dieser Niedergang auch das Überleben der Bonobos. ⊢ ⊢ However, a more accurate translation would be: ⊢ Unüberraschenderweise gefährdet diese Zerstörung auch das Überleben der Bonobos.

2. llama3:8b: ... English: But still it was a real footrace against the other volunteers to get to the captain in charge to find out what our assignments would be. ⊢ Spanish: Based on the provided examples, here is a possible

translation: ⊢ ⊢ Spanish: Pero todavía fue un verdadero carrera contra los otros voluntarios para llegar al capitán al mando y encontrar qué serían nuestras asignaciones. ⊢ ⊢ This translation takes into account the nuances of the original sentence... (explanation continues)

# B Generation Details

## B.1 Inference Hyperparameters

In all experiments, text generation uses multilingual sampling, with default parameters provided by the Ollama library: top-K 40 sampling ($K = 40$) and a temperature of 0.8. Generation stops after 512 tokens or when <EOT> (end of turn) token is printed.

## B.2 Prompt Templates

The construction of templates is depicted in Table 6. The prompts are created by concatenating prompt parts (1–6).

## B.3 Pool Statistics

The statistics of each pool for all pairs of languages studied is in Table 7. We observe a similar trend across all language pairs.

# C Match vs No-match

The comparison of match-vs-non-match for all languages is depicted in Table 8. Figure 6 shows the proportion of isometric outputs given sentences where each of the models failed to produce isometric translation by default, i.e. under the *Random-Random* setting, even across 10 default runs. The translations are taken after only one attempt, which is in contrast to Figure 2 where the outputs are selected from 10 attempts.

# D Few-shot Prompting

The comparison between all few shot settings for all languages is displayed in Figure 8. Additionally, we provide a more detailed view of the results of all few-shot settings, which is presented in Table 9 (zero-shot), Table 10 (5-shot), Table 11 (10-shot) and Table 12 (20-shot). We also compare these results to an oracle setup, in which the best translation is selected based on the sentence BLEU score across all configurations, after filtering out translations that do not fall within ±10% of the source character count.
<table><caption>Table 6: Zero-shot (upper) and few-shot (lower) prompt templates. - stands for new line. Actual prompts are constructed by sequentially concatenating prompt parts (1–6).</caption><thead><tr><th>Part</th><th>Prompt type</th><th>Zero-shot</th></tr></thead><tbody><tr><td>1</td><td>-</td><td>Translate the following text from [src lang] into [tgt lang]</td></tr><tr><td>2</td><td>Random<br>Isometric<br>Same<br>Short / Tiny</td><td>ensuring that it is within ±10% of the character count of the source.<br>ensuring that it has the same length as the source.<br>ensuring that it is shorter than the source.</td></tr><tr><td>3</td><td>No<br>Yes</td><td>Output only the translation. -</td></tr><tr><td>4</td><td>-</td><td>[src lang]: [src sentence] - [tgt lang]:</td></tr></tbody><thead><tr><th>Part</th><th>Prompt type</th><th>Few-shot</th></tr></thead><tbody><tr><td>1</td><td>-</td><td>Here are examples of translations in [tgt lang]</td></tr><tr><td>2</td><td>Random<br>Isometric<br>Same<br>Short / Tiny</td><td>of the source in [src lang]: -<br>that are within ±10% of the character count of the source in [src lang]: -<br>that have the same length as the source in [src lang]: -<br>that are shorter than the source in [src lang]: -</td></tr><tr><td>3</td><td>-</td><td>N × {[src lang]: [src sentence] - [tgt lang]: [tgt sentence] -}</td></tr><tr><td>4</td><td>-</td><td>Provide translation for the following sentence given the examples above.</td></tr><tr><td>5</td><td>No<br>Yes</td><td>Output only the translation. -</td></tr><tr><td>6</td><td>-</td><td>[src lang]: [src sentence] - [tgt lang]:</td></tr></tbody></table>

<table><caption>Table 7: Statistics of pools: The number of samples, minimum and maximum target/source length ratio, and its average and standard deviation.</caption><thead><tr><th colspan="6">En-De</th></tr><tr><th>Pool type</th><th>Count</th><th>Min</th><th>Max</th><th>avg±std</th><th></th></tr></thead><tbody><tr><td>Random</td><td>1415</td><td>0.43</td><td>5.80</td><td>1.14±0.27</td><td></td></tr><tr><td>Isometric</td><td>537</td><td>0.90</td><td>1.10</td><td>1.02±0.05</td><td></td></tr><tr><td>Same</td><td>50</td><td>0.99</td><td>1.00</td><td>1.00±0.00</td><td></td></tr><tr><td>Short</td><td>343</td><td>0.43</td><td>1.00</td><td>0.90±0.11</td><td></td></tr><tr><td>Tiny</td><td>50</td><td>0.43</td><td>0.81</td><td>0.68±0.11</td><td></td></tr></tbody><thead><tr><th colspan="6">En-Fr</th></tr><tr><th>Pool type</th><th>Count</th><th>Min</th><th>Max</th><th>avg±std</th><th></th></tr></thead><tbody><tr><td>Random</td><td>1412</td><td>0.29</td><td>4.90</td><td>1.14±0.28</td><td></td></tr><tr><td>Isometric</td><td>505</td><td>0.90</td><td>1.10</td><td>1.02±0.05</td><td></td></tr><tr><td>Same</td><td>50</td><td>0.99</td><td>1.00</td><td>1.00±0.00</td><td></td></tr><tr><td>Short</td><td>348</td><td>0.29</td><td>1.00</td><td>0.88±0.14</td><td></td></tr><tr><td>Tiny</td><td>50</td><td>0.29</td><td>0.76</td><td>0.60±0.13</td><td></td></tr></tbody><thead><tr><th colspan="6">En-Es</th></tr><tr><th>Pool type</th><th>Count</th><th>Min</th><th>Max</th><th>avg±std</th><th></th></tr></thead><tbody><tr><td>Random</td><td>1316</td><td>0.30</td><td>5.70</td><td>1.08±0.32</td><td></td></tr><tr><td>Isometric</td><td>659</td><td>0.90</td><td>1.10</td><td>1.01±0.05</td><td></td></tr><tr><td>Same</td><td>50</td><td>1.00</td><td>1.00</td><td>1.00±0.00</td><td></td></tr><tr><td>Short</td><td>490</td><td>0.30</td><td>1.00</td><td>0.89±0.12</td><td></td></tr><tr><td>Tiny</td><td>50</td><td>0.30</td><td>0.72</td><td>0.59±0.12</td><td></td></tr></tbody></table>

<figure><img src="image_8.png" /><figcaption>Figure 6: The percentage of input sentences (across all language directions) for which the generated translation meets the isometric condition when the model is prompted to produce isometric, same-length, short, and tiny outputs aligned with respective 5-shot demonstration sets. This evaluation is restricted to input sentences where the particular model did not generate any isometric translation in ten attempts using the uncontrolled prompt.</figcaption></figure>

## E Translation Quality and Length Tradeoff

The length ratio and translation quality tradeoff for all languages is presented in Figure 7. We observe that models generally produce isometric translation when *Tiny* setting is used. The exception is Spanish, where the average 1.0 length ratio can be obtained by *Short* setting. This is in line with our intuition since Spanish exhibits a smaller length ratio of 1.04 for the training data from MuST-C, compared

to length ratios of 1.12 and 1.11 for German and French, respectively.<fnref n="9" />

<footnote n="9">⁹These values were calculated by the organizers of the isometric shared task and are mentioned on the official website https://iwslt.org/2022/isometric.</footnote>
<figure><img src="image_9.png" /><figcaption>Figure 7: Trade-off between the length ratio (x-axis) and translation quality (y-axis) for 5, 10, 20-shot settings and all models and language pairs.</figcaption></figure>
En-De

<table><thead><tr><th rowspan="2">Model</th><th colspan="2">Random</th><th colspan="2">Isometric</th><th colspan="2">Same</th><th colspan="2">Short</th><th colspan="2">Tiny</th></tr><tr><th>No</th><th>Yes</th><th>No</th><th>Yes</th><th>No</th><th>Yes</th><th>No</th><th>Yes</th><th>No</th><th>Yes</th></tr></thead><tbody><tr><td>gemma2:27b</td><td>1.100</td><td>1.097</td><td>1.099</td><td>1.094</td><td>1.098</td><td>1.097</td><td><u>1.087</u></td><td><u>1.011</u></td><td><u>1.066</u></td><td><u>0.955</u></td></tr><tr><td>gemma2:9b</td><td>1.108</td><td>1.106</td><td>1.106</td><td>1.101</td><td><u>1.106</u></td><td><u>1.073</u></td><td><u>1.099</u></td><td><u>1.026</u></td><td><u>1.080</u></td><td><u>0.981</u></td></tr><tr><td>llama3:70b</td><td>1.149</td><td>1.151</td><td>1.149</td><td>1.139</td><td>1.141</td><td>1.134</td><td><u>1.138</u></td><td><u>1.005</u></td><td><u>1.122</u></td><td><u>0.905</u></td></tr><tr><td>llama3:8b</td><td>1.106</td><td>1.100</td><td>1.093</td><td>1.108</td><td>1.099</td><td>1.112</td><td><u>1.085</u></td><td><u>1.048</u></td><td><u>1.056</u></td><td><u>0.994</u></td></tr><tr><td>mistral:7b</td><td>1.133</td><td>1.129</td><td>1.126</td><td>1.128</td><td>1.135</td><td>1.125</td><td><u>1.121</u></td><td><u>1.105</u></td><td><u>1.138</u></td><td><u>1.085</u></td></tr><tr><td>mixtral:8x7b</td><td>1.402</td><td>1.411</td><td>1.375</td><td>1.362</td><td>1.378</td><td>1.381</td><td>1.385</td><td>1.297</td><td>1.363</td><td>1.265</td></tr><tr><td>qwen2:72b</td><td>1.223</td><td>1.169</td><td>1.195</td><td>1.178</td><td>1.184</td><td>1.173</td><td>1.170</td><td>1.128</td><td>1.164</td><td>1.129</td></tr><tr><td>qwen2:7b</td><td>1.132</td><td>1.160</td><td>1.144</td><td>1.125</td><td>1.129</td><td>1.129</td><td>1.128</td><td>1.135</td><td>1.117</td><td>1.095</td></tr></tbody></table>

En-Fr

<table><tbody><tr><td>gemma2:27b</td><td>1.128</td><td>1.126</td><td>1.123</td><td>1.125</td><td>1.127</td><td>1.128</td><td><u>1.115</u></td><td><u>1.034</u></td><td><u>1.087</u></td><td><u>0.970</u></td></tr><tr><td>gemma2:9b</td><td>1.143</td><td>1.146</td><td>1.142</td><td>1.132</td><td>1.144</td><td>1.121</td><td><u>1.133</u></td><td><u>1.062</u></td><td><u>1.116</u></td><td><u>1.021</u></td></tr><tr><td>llama3:70b</td><td>1.178</td><td>1.176</td><td>1.173</td><td>1.167</td><td>1.174</td><td>1.172</td><td><u>1.163</u></td><td><u>1.015</u></td><td><u>1.147</u></td><td><u>0.877</u></td></tr><tr><td>llama3:8b</td><td>1.136</td><td>1.121</td><td>1.134</td><td>1.141</td><td>1.136</td><td>1.144</td><td><u>1.110</u></td><td><u>1.052</u></td><td><u>1.072</u></td><td><u>0.986</u></td></tr><tr><td>mistral:7b</td><td>1.162</td><td>1.166</td><td>1.159</td><td>1.152</td><td>1.167</td><td>1.177</td><td>1.141</td><td>1.127</td><td>1.153</td><td>1.137</td></tr><tr><td>mixtral:8x7b</td><td>1.335</td><td>1.332</td><td><u>1.316</u></td><td><u>1.233</u></td><td><u>1.351</u></td><td><u>1.247</u></td><td><u>1.355</u></td><td><u>1.206</u></td><td><u>1.348</u></td><td><u>1.203</u></td></tr><tr><td>qwen2:72b</td><td>1.178</td><td>1.180</td><td>1.198</td><td>1.174</td><td>1.184</td><td>1.171</td><td><u>1.215</u></td><td><u>1.139</u></td><td><u>1.172</u></td><td><u>1.118</u></td></tr><tr><td>qwen2:7b</td><td>1.183</td><td>1.175</td><td>1.175</td><td>1.171</td><td>1.172</td><td>1.172</td><td>1.170</td><td>1.146</td><td>1.152</td><td>1.124</td></tr></tbody></table>

En-Es

<table><caption>Table 8: Average target/source ratios for every pool type when instructions match ('Yes') or do not match ('No') the properties of the samples in 5-shot prompting. Differences for each pool type with *p*-value < 0.1 are underlined.</caption><tbody><tr><td>gemma2:27b</td><td>1.058</td><td>1.057</td><td>1.057</td><td>1.058</td><td>1.058</td><td>1.054</td><td><u>1.053</u></td><td><u>0.994</u></td><td><u>1.040</u></td><td><u>0.939</u></td></tr><tr><td>gemma2:9b</td><td>1.072</td><td>1.072</td><td>1.071</td><td>1.064</td><td><u>1.070</u></td><td><u>1.046</u></td><td><u>1.067</u></td><td><u>1.018</u></td><td><u>1.054</u></td><td><u>0.969</u></td></tr><tr><td>llama3:70b</td><td>1.086</td><td>1.089</td><td>1.088</td><td>1.086</td><td>1.086</td><td>1.083</td><td><u>1.084</u></td><td><u>0.969</u></td><td><u>1.062</u></td><td><u>0.823</u></td></tr><tr><td>llama3:8b</td><td>1.050</td><td>1.051</td><td>1.055</td><td>1.063</td><td>1.051</td><td>1.062</td><td><u>1.042</u></td><td><u>1.008</u></td><td><u>0.999</u></td><td><u>0.907</u></td></tr><tr><td>mistral:7b</td><td>1.050</td><td>1.058</td><td>1.058</td><td>1.050</td><td>1.078</td><td>1.063</td><td>1.106</td><td>1.038</td><td><u>1.039</u></td><td><u>1.014</u></td></tr><tr><td>mixtral:8x7b</td><td>1.321</td><td>1.287</td><td>1.340</td><td>1.269</td><td>1.265</td><td>1.222</td><td>1.286</td><td>1.254</td><td>1.283</td><td>1.252</td></tr><tr><td>qwen2:72b</td><td>1.116</td><td>1.113</td><td>1.108</td><td>1.110</td><td>1.113</td><td>1.117</td><td>1.114</td><td>1.091</td><td>1.106</td><td>1.075</td></tr><tr><td>qwen2:7b</td><td>1.074</td><td>1.097</td><td>1.074</td><td>1.079</td><td>1.075</td><td>1.074</td><td>1.083</td><td>1.057</td><td><u>1.065</u></td><td><u>1.035</u></td></tr></tbody></table>
<figure><img src="image_10.png" /><figcaption>Figure 8: The translation quality (BERTScore, dashed lines and the right hand y-axes) and length ratio (solid lines and left-hand y-axes) for all few-shot settings, models and language pairs.</figcaption></figure>
<table><caption>Table 9: 0-shot prompting for all language pairs. Columns denote length ratio (LR), length compliance (LC), BERTScore (BS) and BLEU.</caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">Prompt Type</th><th colspan="4">En-De</th><th colspan="4">En-Fr</th><th colspan="4">En-Es</th></tr><tr><th>LR</th><th>LC</th><th>BS</th><th>BLEU</th><th>LR</th><th>LC</th><th>BS</th><th>BLEU</th><th>LR</th><th>LC</th><th>BS</th><th>BLEU</th></tr></thead><tbody><tr><td rowspan="4">gemma2:27b</td><td>Random</td><td>1.13</td><td>37.39</td><td>83.57</td><td>31.82</td><td>1.15</td><td>37.78</td><td>86.80</td><td>43.29</td><td>1.08</td><td>48.20</td><td>86.08</td><td>39.24</td></tr><tr><td>Isometric</td><td>1.12</td><td>39.10</td><td>83.28</td><td>29.94</td><td>1.14</td><td>39.10</td><td>85.70</td><td>38.25</td><td>1.06</td><td>51.65</td><td>85.65</td><td>37.54</td></tr><tr><td>Same</td><td>1.12</td><td>41.55</td><td>83.52</td><td>31.02</td><td>1.13</td><td>44.45</td><td>86.18</td><td>40.07</td><td>1.07</td><td>51.95</td><td>85.92</td><td>37.75</td></tr><tr><td>Short</td><td>0.83</td><td>27.05</td><td>76.10</td><td>12.16</td><td>0.86</td><td>33.85</td><td>77.74</td><td>15.91</td><td>0.82</td><td>30.25</td><td>79.88</td><td>19.15</td></tr><tr><td rowspan="4">gemma2:9b</td><td>Random</td><td>1.13</td><td>34.85</td><td>83.43</td><td>31.47</td><td>1.15</td><td>36.75</td><td>86.40</td><td>41.81</td><td>1.08</td><td>48.85</td><td>85.54</td><td>37.87</td></tr><tr><td>Isometric</td><td>1.12</td><td>38.75</td><td>82.93</td><td>29.37</td><td>1.14</td><td>38.70</td><td>85.73</td><td>38.18</td><td>1.07</td><td>54.40</td><td>85.60</td><td>36.59</td></tr><tr><td>Same</td><td>1.11</td><td>41.45</td><td>82.89</td><td>29.63</td><td>1.13</td><td>40.75</td><td>85.94</td><td>39.55</td><td>1.05</td><td>55.95</td><td>85.80</td><td>36.91</td></tr><tr><td>Short</td><td>0.91</td><td>35.65</td><td>78.15</td><td>16.12</td><td>0.93</td><td>38.65</td><td>79.45</td><td>20.16</td><td>0.87</td><td>36.45</td><td>80.70</td><td>21.66</td></tr><tr><td rowspan="4">llama3:70b</td><td>Random</td><td>1.18</td><td>26.94</td><td>84.58</td><td>34.31</td><td>1.19</td><td>28.06</td><td>87.52</td><td>44.24</td><td>1.11</td><td>45.83</td><td>85.78</td><td>37.08</td></tr><tr><td>Isometric</td><td>1.16</td><td>31.80</td><td>84.53</td><td>33.79</td><td>1.17</td><td>31.40</td><td>87.42</td><td>43.65</td><td>1.09</td><td>49.65</td><td>85.95</td><td>37.36</td></tr><tr><td>Same</td><td>1.17</td><td>26.70</td><td>84.75</td><td>34.87</td><td>1.19</td><td>28.35</td><td>87.58</td><td>44.24</td><td>1.10</td><td>46.95</td><td>85.87</td><td>36.72</td></tr><tr><td>Short</td><td>0.92</td><td>38.75</td><td>79.04</td><td>17.20</td><td>0.93</td><td>38.40</td><td>80.26</td><td>21.27</td><td>0.88</td><td>37.05</td><td>80.85</td><td>23.17</td></tr><tr><td rowspan="4">llama3:8b</td><td>Random</td><td>1.16</td><td>31.60</td><td>82.70</td><td>28.96</td><td>1.18</td><td>31.39</td><td>84.81</td><td>36.68</td><td>1.08</td><td>50.10</td><td>83.93</td><td>32.78</td></tr><tr><td>Isometric</td><td>1.18</td><td>29.33</td><td>82.23</td><td>27.82</td><td>1.18</td><td>29.67</td><td>83.90</td><td>34.23</td><td>1.11</td><td>47.00</td><td>83.10</td><td>31.00</td></tr><tr><td>Same</td><td>1.17</td><td>28.65</td><td>82.55</td><td>28.36</td><td>1.18</td><td>27.50</td><td>84.01</td><td>34.70</td><td>1.08</td><td>47.33</td><td>82.96</td><td>30.99</td></tr><tr><td>Short</td><td>0.96</td><td>33.85</td><td>77.88</td><td>15.83</td><td>0.95</td><td>37.10</td><td>78.92</td><td>18.29</td><td>0.90</td><td>39.70</td><td>79.61</td><td>19.69</td></tr><tr><td rowspan="4">mistral:7b</td><td>Random</td><td>1.20</td><td>30.40</td><td>77.28</td><td>23.33</td><td>1.21</td><td>32.40</td><td>82.65</td><td>30.19</td><td>1.09</td><td>48.70</td><td>82.52</td><td>29.49</td></tr><tr><td>Isometric</td><td>1.29</td><td>24.28</td><td>72.26</td><td>19.88</td><td>1.29</td><td>26.67</td><td>80.79</td><td>28.20</td><td>1.15</td><td>43.89</td><td>81.92</td><td>28.57</td></tr><tr><td>Same</td><td>1.18</td><td>28.40</td><td>80.55</td><td>23.23</td><td>1.20</td><td>32.90</td><td>82.66</td><td>29.99</td><td>1.09</td><td>47.40</td><td>82.79</td><td>29.30</td></tr><tr><td>Short</td><td>1.13</td><td>39.40</td><td>73.62</td><td>16.99</td><td>1.12</td><td>41.30</td><td>78.78</td><td>23.39</td><td>0.97</td><td>45.55</td><td>79.23</td><td>23.13</td></tr><tr><td rowspan="4">mixtral:8x7b</td><td>Random</td><td>1.42</td><td>23.75</td><td>81.50</td><td>29.00</td><td>1.27</td><td>26.00</td><td>85.45</td><td>38.28</td><td>1.36</td><td>38.83</td><td>83.72</td><td>31.61</td></tr><tr><td>Isometric</td><td>1.49</td><td>15.89</td><td>79.81</td><td>26.21</td><td>1.41</td><td>18.85</td><td>82.65</td><td>34.47</td><td>1.30</td><td>29.50</td><td>82.48</td><td>30.23</td></tr><tr><td>Same</td><td>1.53</td><td>21.50</td><td>79.77</td><td>26.86</td><td>1.31</td><td>28.78</td><td>83.94</td><td>36.18</td><td>1.30</td><td>39.45</td><td>83.24</td><td>30.73</td></tr><tr><td>Short</td><td>1.81</td><td>18.50</td><td>73.92</td><td>18.32</td><td>1.63</td><td>28.30</td><td>77.58</td><td>26.18</td><td>1.70</td><td>26.30</td><td>75.75</td><td>20.91</td></tr><tr><td rowspan="4">qwen2:72b</td><td>Random</td><td>1.17</td><td>30.28</td><td>84.02</td><td>33.07</td><td>1.18</td><td>33.61</td><td>87.50</td><td>44.30</td><td>1.11</td><td>45.44</td><td>85.83</td><td>37.82</td></tr><tr><td>Isometric</td><td>1.15</td><td>35.65</td><td>83.69</td><td>31.80</td><td>1.17</td><td>38.15</td><td>86.76</td><td>41.92</td><td>1.09</td><td>50.60</td><td>85.53</td><td>36.41</td></tr><tr><td>Same</td><td>1.16</td><td>32.50</td><td>83.62</td><td>32.22</td><td>1.18</td><td>38.45</td><td>87.35</td><td>44.87</td><td>1.10</td><td>50.70</td><td>85.73</td><td>36.86</td></tr><tr><td>Short</td><td>0.98</td><td>40.10</td><td>80.48</td><td>20.71</td><td>1.01</td><td>43.95</td><td>83.46</td><td>27.91</td><td>0.92</td><td>37.60</td><td>83.27</td><td>25.77</td></tr><tr><td rowspan="4">qwen2:7b</td><td>Random</td><td>1.21</td><td>28.55</td><td>80.83</td><td>24.32</td><td>1.19</td><td>28.81</td><td>84.20</td><td>36.04</td><td>1.10</td><td>41.45</td><td>83.04</td><td>30.98</td></tr><tr><td>Isometric</td><td>1.20</td><td>27.50</td><td>80.51</td><td>23.05</td><td>1.19</td><td>30.75</td><td>83.61</td><td>34.00</td><td>1.10</td><td>45.72</td><td>83.19</td><td>30.33</td></tr><tr><td>Same</td><td>1.19</td><td>28.45</td><td>81.04</td><td>23.67</td><td>1.18</td><td>29.80</td><td>84.27</td><td>35.42</td><td>1.10</td><td>43.90</td><td>83.55</td><td>30.91</td></tr><tr><td>Short</td><td>1.09</td><td>38.00</td><td>79.34</td><td>18.89</td><td>1.11</td><td>41.55</td><td>82.78</td><td>30.33</td><td>1.02</td><td>49.65</td><td>82.54</td><td>27.35</td></tr><tr><td colspan="2">Oracle</td><td>1.07</td><td>65.00</td><td>87.74</td><td>49.60</td><td>1.05</td><td>76.50</td><td>87.99</td><td>55.60</td><td>1.03</td><td>83.00</td><td>88.47</td><td>53.50</td></tr></tbody></table>
<table><caption>Table 10: 5-shot prompting for all language pairs when sampling examples from different pools. Columns denote length ratio (LR), length compliance (LC), BERTScore (BS) and BLEU. All numbers are averaged across 10 instances. The prompt text *matches* the pool type.</caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">Pool Type</th><th colspan="4">En-De</th><th colspan="4">En-Fr</th><th colspan="4">En-Es</th></tr><tr><th>LR</th><th>LC</th><th>BS</th><th>BLEU</th><th>LR</th><th>LC</th><th>BS</th><th>BLEU</th><th>LR</th><th>LC</th><th>BS</th><th>BLEU</th></tr></thead><tbody><tr><td rowspan="5">gemma2:27b</td><td>Random</td><td>1.13</td><td>36.75</td><td>83.97</td><td>32.81</td><td>1.14</td><td>39.70</td><td>87.01</td><td>42.86</td><td>1.08</td><td>50.60</td><td>86.11</td><td>39.02</td></tr><tr><td>Isometric</td><td>1.13</td><td>39.15</td><td>84.04</td><td>33.07</td><td>1.14</td><td>40.35</td><td>86.95</td><td>42.29</td><td>1.08</td><td>51.55</td><td>86.10</td><td>38.61</td></tr><tr><td>Same</td><td>1.13</td><td>38.65</td><td>84.18</td><td>33.05</td><td>1.14</td><td>40.35</td><td>87.58</td><td>43.81</td><td>1.07</td><td>52.50</td><td>86.29</td><td>38.71</td></tr><tr><td>Short</td><td>1.04</td><td>44.05</td><td>82.41</td><td>28.01</td><td>1.05</td><td>42.65</td><td>84.50</td><td>34.61</td><td>1.01</td><td>53.15</td><td>85.06</td><td>35.54</td></tr><tr><td>Tiny</td><td>0.99</td><td>41.00</td><td>81.13</td><td>24.46</td><td>0.99</td><td>42.55</td><td>82.83</td><td>30.89</td><td>0.96</td><td>48.05</td><td>83.81</td><td>32.21</td></tr><tr><td rowspan="5">gemma2:9b</td><td>Random</td><td>1.14</td><td>35.35</td><td>84.07</td><td>32.37</td><td>1.16</td><td>37.85</td><td>87.09</td><td>43.13</td><td>1.09</td><td>50.90</td><td>85.99</td><td>37.59</td></tr><tr><td>Isometric</td><td>1.14</td><td>37.25</td><td>83.93</td><td>31.80</td><td>1.15</td><td>40.30</td><td>86.76</td><td>42.09</td><td>1.08</td><td>51.85</td><td>86.00</td><td>37.59</td></tr><tr><td>Same</td><td>1.11</td><td>41.35</td><td>83.72</td><td>30.57</td><td>1.14</td><td>43.00</td><td>86.77</td><td>42.05</td><td>1.06</td><td>56.50</td><td>85.75</td><td>37.09</td></tr><tr><td>Short</td><td>1.06</td><td>44.25</td><td>82.71</td><td>28.24</td><td>1.08</td><td>46.50</td><td>85.05</td><td>36.13</td><td>1.04</td><td>56.60</td><td>85.54</td><td>36.71</td></tr><tr><td>Tiny</td><td>1.01</td><td>44.50</td><td>81.55</td><td>25.40</td><td>1.04</td><td>44.10</td><td>84.13</td><td>34.59</td><td>0.99</td><td>53.80</td><td>84.55</td><td>33.53</td></tr><tr><td rowspan="5">llama3:70b</td><td>Random</td><td>1.19</td><td>26.45</td><td>84.85</td><td>34.86</td><td>1.19</td><td>29.15</td><td>87.40</td><td>44.08</td><td>1.11</td><td>48.45</td><td>85.62</td><td>36.89</td></tr><tr><td>Isometric</td><td>1.17</td><td>27.90</td><td>84.65</td><td>34.25</td><td>1.18</td><td>30.85</td><td>87.37</td><td>43.63</td><td>1.10</td><td>47.90</td><td>85.66</td><td>37.07</td></tr><tr><td>Same</td><td>1.17</td><td>28.50</td><td>84.83</td><td>34.84</td><td>1.19</td><td>31.65</td><td>87.50</td><td>43.96</td><td>1.10</td><td>49.60</td><td>85.52</td><td>36.43</td></tr><tr><td>Short</td><td>1.04</td><td>40.90</td><td>82.76</td><td>28.12</td><td>1.03</td><td>44.00</td><td>84.81</td><td>35.36</td><td>0.99</td><td>50.15</td><td>84.51</td><td>33.68</td></tr><tr><td>Tiny</td><td>0.94</td><td>35.80</td><td>80.61</td><td>22.66</td><td>0.89</td><td>37.05</td><td>81.20</td><td>25.79</td><td>0.84</td><td>33.75</td><td>80.97</td><td>24.73</td></tr><tr><td rowspan="5">llama3:8b</td><td>Random</td><td>1.14</td><td>32.00</td><td>81.55</td><td>26.44</td><td>1.14</td><td>34.22</td><td>83.57</td><td>34.32</td><td>1.07</td><td>48.70</td><td>82.29</td><td>30.53</td></tr><tr><td>Isometric</td><td>1.15</td><td>32.00</td><td>81.87</td><td>26.57</td><td>1.16</td><td>36.30</td><td>83.74</td><td>34.08</td><td>1.08</td><td>49.40</td><td>83.16</td><td>31.45</td></tr><tr><td>Same</td><td>1.15</td><td>33.40</td><td>82.06</td><td>27.04</td><td>1.16</td><td>34.50</td><td>84.28</td><td>34.80</td><td>1.08</td><td>51.45</td><td>83.41</td><td>31.05</td></tr><tr><td>Short</td><td>1.08</td><td>39.05</td><td>80.69</td><td>23.71</td><td>1.07</td><td>38.55</td><td>81.59</td><td>29.35</td><td>1.03</td><td>51.40</td><td>81.88</td><td>28.56</td></tr><tr><td>Tiny</td><td>1.03</td><td>37.55</td><td>79.41</td><td>21.21</td><td>1.00</td><td>38.10</td><td>80.24</td><td>26.74</td><td>0.93</td><td>43.15</td><td>79.55</td><td>23.71</td></tr><tr><td rowspan="5">mistral:7b</td><td>Random</td><td>1.17</td><td>32.00</td><td>80.66</td><td>23.66</td><td>1.18</td><td>35.20</td><td>82.29</td><td>29.70</td><td>1.08</td><td>48.78</td><td>82.28</td><td>28.52</td></tr><tr><td>Isometric</td><td>1.17</td><td>32.55</td><td>80.76</td><td>23.67</td><td>1.17</td><td>37.40</td><td>82.50</td><td>30.11</td><td>1.07</td><td>50.80</td><td>82.57</td><td>28.79</td></tr><tr><td>Same</td><td>1.16</td><td>33.50</td><td>80.93</td><td>23.86</td><td>1.19</td><td>34.90</td><td>82.37</td><td>29.91</td><td>1.08</td><td>52.60</td><td>82.64</td><td>28.99</td></tr><tr><td>Short</td><td>1.14</td><td>38.15</td><td>80.30</td><td>22.68</td><td>1.14</td><td>39.10</td><td>82.01</td><td>29.06</td><td>1.06</td><td>51.85</td><td>81.96</td><td>28.64</td></tr><tr><td>Tiny</td><td>1.12</td><td>38.65</td><td>79.75</td><td>21.46</td><td>1.15</td><td>40.00</td><td>81.34</td><td>28.10</td><td>1.03</td><td>52.25</td><td>81.42</td><td>27.36</td></tr><tr><td rowspan="5">mixtral:8x7b</td><td>Random</td><td>1.43</td><td>26.85</td><td>81.99</td><td>30.58</td><td>1.33</td><td>30.00</td><td>84.20</td><td>38.51</td><td>1.30</td><td>40.45</td><td>83.68</td><td>33.44</td></tr><tr><td>Isometric</td><td>1.38</td><td>29.20</td><td>82.02</td><td>30.71</td><td>1.24</td><td>32.10</td><td>84.64</td><td>38.40</td><td>1.28</td><td>42.80</td><td>83.78</td><td>33.85</td></tr><tr><td>Same</td><td>1.40</td><td>32.40</td><td>82.38</td><td>31.10</td><td>1.26</td><td>32.35</td><td>84.74</td><td>39.20</td><td>1.24</td><td>44.45</td><td>84.12</td><td>34.18</td></tr><tr><td>Short</td><td>1.32</td><td>33.65</td><td>81.21</td><td>28.83</td><td>1.21</td><td>37.45</td><td>83.77</td><td>35.39</td><td>1.26</td><td>45.75</td><td>82.91</td><td>32.28</td></tr><tr><td>Tiny</td><td>1.29</td><td>36.80</td><td>79.98</td><td>26.16</td><td>1.21</td><td>38.56</td><td>82.70</td><td>33.03</td><td>1.25</td><td>46.80</td><td>82.11</td><td>31.02</td></tr><tr><td rowspan="5">qwen2:72b</td><td>Random</td><td>1.20</td><td>29.25</td><td>84.08</td><td>33.25</td><td>1.20</td><td>33.35</td><td>87.18</td><td>43.74</td><td>1.13</td><td>45.65</td><td>85.48</td><td>37.00</td></tr><tr><td>Isometric</td><td>1.21</td><td>29.95</td><td>83.98</td><td>33.02</td><td>1.19</td><td>34.60</td><td>86.85</td><td>42.69</td><td>1.13</td><td>46.10</td><td>85.37</td><td>36.77</td></tr><tr><td>Same</td><td>1.21</td><td>31.20</td><td>83.98</td><td>32.91</td><td>1.19</td><td>35.80</td><td>87.26</td><td>43.57</td><td>1.14</td><td>48.60</td><td>85.66</td><td>37.52</td></tr><tr><td>Short</td><td>1.16</td><td>34.40</td><td>83.63</td><td>31.59</td><td>1.15</td><td>41.60</td><td>86.36</td><td>40.17</td><td>1.11</td><td>49.65</td><td>85.05</td><td>36.10</td></tr><tr><td>Tiny</td><td>1.16</td><td>35.75</td><td>83.03</td><td>30.09</td><td>1.13</td><td>42.80</td><td>85.86</td><td>38.80</td><td>1.10</td><td>49.40</td><td>84.52</td><td>35.20</td></tr><tr><td rowspan="5">qwen2:7b</td><td>Random</td><td>1.20</td><td>32.10</td><td>81.04</td><td>23.99</td><td>1.19</td><td>31.75</td><td>84.24</td><td>35.37</td><td>1.11</td><td>47.40</td><td>83.53</td><td>30.89</td></tr><tr><td>Isometric</td><td>1.16</td><td>30.30</td><td>81.24</td><td>23.78</td><td>1.19</td><td>32.30</td><td>84.11</td><td>34.86</td><td>1.10</td><td>48.70</td><td>83.63</td><td>31.13</td></tr><tr><td>Same</td><td>1.17</td><td>31.55</td><td>81.28</td><td>24.07</td><td>1.19</td><td>32.80</td><td>84.41</td><td>35.16</td><td>1.09</td><td>47.75</td><td>83.52</td><td>30.98</td></tr><tr><td>Short</td><td>1.14</td><td>35.56</td><td>80.97</td><td>22.99</td><td>1.16</td><td>35.25</td><td>83.77</td><td>34.09</td><td>1.08</td><td>48.95</td><td>83.33</td><td>30.75</td></tr><tr><td>Tiny</td><td>1.13</td><td>34.89</td><td>80.63</td><td>22.43</td><td>1.14</td><td>36.60</td><td>82.56</td><td>32.26</td><td>1.05</td><td>50.00</td><td>82.89</td><td>29.95</td></tr><tr><td>Oracle</td><td></td><td>1.07</td><td>65.00</td><td>87.74</td><td>49.60</td><td>1.05</td><td>76.50</td><td>87.99</td><td>55.60</td><td>1.03</td><td>83.00</td><td>88.47</td><td>53.50</td></tr></tbody></table>
<table><caption>Table 11: 10-shot prompting for all language pairs when sampling examples from different pools. Columns denote length ratio (LR), length compliance (LC), BERTScore (BS) and BLEU. All numbers are averaged across 10 instances. The prompt text *matches* the pool type.</caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">Pool Type</th><th colspan="4">En-De</th><th colspan="4">En-Fr</th><th colspan="4">En-Es</th></tr><tr><th>LR</th><th>LC</th><th>BS</th><th>BLEU</th><th>LR</th><th>LC</th><th>BS</th><th>BLEU</th><th>LR</th><th>LC</th><th>BS</th><th>BLEU</th></tr></thead><tbody><tr><td rowspan="5">gemma2:27b</td><td>Random</td><td>1.13</td><td>37.20</td><td>84.14</td><td>33.34</td><td>1.14</td><td>41.50</td><td>86.93</td><td>42.54</td><td>1.08</td><td>50.65</td><td>86.01</td><td>38.58</td></tr><tr><td>Isometric</td><td>1.13</td><td>39.10</td><td>83.98</td><td>32.89</td><td>1.14</td><td>40.35</td><td>86.79</td><td>41.72</td><td>1.08</td><td>50.55</td><td>86.03</td><td>38.50</td></tr><tr><td>Same</td><td>1.13</td><td>38.80</td><td>84.19</td><td>33.31</td><td>1.14</td><td>41.70</td><td>87.45</td><td>43.59</td><td>1.08</td><td>50.90</td><td>86.36</td><td>39.01</td></tr><tr><td>Short</td><td>1.05</td><td>43.95</td><td>82.76</td><td>28.38</td><td>1.06</td><td>45.40</td><td>85.04</td><td>36.26</td><td>1.02</td><td>52.15</td><td>85.29</td><td>36.17</td></tr><tr><td>Tiny</td><td>1.00</td><td>41.05</td><td>81.25</td><td>24.84</td><td>1.01</td><td>41.55</td><td>83.44</td><td>33.10</td><td>0.97</td><td>47.70</td><td>84.10</td><td>33.15</td></tr><tr><td rowspan="5">gemma2:9b</td><td>Random</td><td>1.14</td><td>36.75</td><td>83.91</td><td>32.23</td><td>1.16</td><td>39.85</td><td>86.95</td><td>43.22</td><td>1.09</td><td>50.35</td><td>86.05</td><td>37.93</td></tr><tr><td>Isometric</td><td>1.13</td><td>36.35</td><td>83.89</td><td>31.57</td><td>1.15</td><td>41.40</td><td>86.76</td><td>41.89</td><td>1.08</td><td>53.05</td><td>86.00</td><td>37.68</td></tr><tr><td>Same</td><td>1.12</td><td>40.15</td><td>83.80</td><td>30.88</td><td>1.14</td><td>42.45</td><td>86.86</td><td>42.48</td><td>1.07</td><td>55.40</td><td>85.93</td><td>37.53</td></tr><tr><td>Short</td><td>1.07</td><td>43.55</td><td>82.81</td><td>28.84</td><td>1.08</td><td>47.35</td><td>85.36</td><td>37.34</td><td>1.04</td><td>58.45</td><td>85.57</td><td>36.41</td></tr><tr><td>Tiny</td><td>1.03</td><td>43.85</td><td>81.92</td><td>26.68</td><td>1.03</td><td>43.95</td><td>83.80</td><td>34.25</td><td>1.00</td><td>55.35</td><td>84.63</td><td>33.89</td></tr><tr><td rowspan="5">llama3:70b</td><td>Random</td><td>1.19</td><td>26.30</td><td>84.87</td><td>35.16</td><td>1.19</td><td>30.10</td><td>87.39</td><td>44.23</td><td>1.11</td><td>47.15</td><td>85.67</td><td>36.88</td></tr><tr><td>Isometric</td><td>1.18</td><td>27.70</td><td>84.79</td><td>34.74</td><td>1.18</td><td>30.75</td><td>87.45</td><td>43.84</td><td>1.10</td><td>46.75</td><td>85.68</td><td>36.90</td></tr><tr><td>Same</td><td>1.17</td><td>28.90</td><td>85.00</td><td>35.04</td><td>1.18</td><td>31.45</td><td>87.65</td><td>44.37</td><td>1.10</td><td>48.95</td><td>85.67</td><td>36.73</td></tr><tr><td>Short</td><td>1.05</td><td>40.00</td><td>82.94</td><td>28.86</td><td>1.02</td><td>42.45</td><td>84.49</td><td>34.89</td><td>1.00</td><td>50.05</td><td>84.44</td><td>34.19</td></tr><tr><td>Tiny</td><td>0.95</td><td>36.10</td><td>81.01</td><td>23.37</td><td>0.88</td><td>36.50</td><td>80.87</td><td>25.85</td><td>0.84</td><td>34.95</td><td>80.90</td><td>25.09</td></tr><tr><td rowspan="5">llama3:8b</td><td>Random</td><td>1.14</td><td>33.30</td><td>81.07</td><td>25.66</td><td>1.14</td><td>34.22</td><td>83.37</td><td>33.56</td><td>1.06</td><td>49.20</td><td>82.29</td><td>30.24</td></tr><tr><td>Isometric</td><td>1.14</td><td>33.20</td><td>81.77</td><td>26.21</td><td>1.14</td><td>36.06</td><td>83.75</td><td>34.38</td><td>1.08</td><td>49.55</td><td>82.85</td><td>31.05</td></tr><tr><td>Same</td><td>1.15</td><td>33.80</td><td>81.88</td><td>26.53</td><td>1.15</td><td>34.05</td><td>83.97</td><td>34.57</td><td>1.07</td><td>52.00</td><td>83.40</td><td>31.27</td></tr><tr><td>Short</td><td>1.06</td><td>36.35</td><td>80.16</td><td>22.92</td><td>1.04</td><td>39.60</td><td>81.31</td><td>29.40</td><td>1.00</td><td>48.00</td><td>81.31</td><td>27.55</td></tr><tr><td>Tiny</td><td>1.00</td><td>35.75</td><td>78.99</td><td>20.19</td><td>0.95</td><td>35.90</td><td>79.07</td><td>24.33</td><td>0.89</td><td>42.15</td><td>78.70</td><td>22.47</td></tr><tr><td rowspan="5">mistral:7b</td><td>Random</td><td>1.16</td><td>32.40</td><td>80.52</td><td>23.18</td><td>1.17</td><td>34.33</td><td>82.56</td><td>30.17</td><td>1.08</td><td>47.65</td><td>82.06</td><td>28.27</td></tr><tr><td>Isometric</td><td>1.15</td><td>32.95</td><td>80.79</td><td>23.87</td><td>1.17</td><td>36.83</td><td>82.36</td><td>29.99</td><td>1.07</td><td>49.60</td><td>82.39</td><td>28.73</td></tr><tr><td>Same</td><td>1.16</td><td>33.45</td><td>80.85</td><td>23.59</td><td>1.17</td><td>36.40</td><td>82.54</td><td>30.25</td><td>1.09</td><td>50.20</td><td>82.77</td><td>29.37</td></tr><tr><td>Short</td><td>1.13</td><td>37.10</td><td>80.45</td><td>22.91</td><td>1.15</td><td>38.35</td><td>81.97</td><td>29.10</td><td>1.05</td><td>52.05</td><td>82.19</td><td>28.43</td></tr><tr><td>Tiny</td><td>1.13</td><td>37.85</td><td>79.66</td><td>21.53</td><td>1.13</td><td>41.25</td><td>81.13</td><td>28.11</td><td>1.04</td><td>51.40</td><td>81.65</td><td>27.74</td></tr><tr><td rowspan="5">mixtral:8x7b</td><td>Random</td><td>1.46</td><td>28.15</td><td>82.02</td><td>30.81</td><td>1.32</td><td>31.60</td><td>84.45</td><td>38.81</td><td>1.40</td><td>40.35</td><td>83.22</td><td>33.38</td></tr><tr><td>Isometric</td><td>1.35</td><td>31.95</td><td>82.21</td><td>31.09</td><td>1.25</td><td>31.75</td><td>84.52</td><td>38.81</td><td>1.36</td><td>42.15</td><td>83.37</td><td>33.56</td></tr><tr><td>Same</td><td>1.42</td><td>30.95</td><td>81.95</td><td>30.69</td><td>1.28</td><td>33.75</td><td>84.73</td><td>38.92</td><td>1.28</td><td>43.20</td><td>83.92</td><td>34.50</td></tr><tr><td>Short</td><td>1.36</td><td>35.75</td><td>81.16</td><td>28.92</td><td>1.24</td><td>37.15</td><td>83.72</td><td>35.97</td><td>1.30</td><td>46.45</td><td>82.89</td><td>32.66</td></tr><tr><td>Tiny</td><td>1.27</td><td>39.20</td><td>80.21</td><td>26.27</td><td>1.23</td><td>37.80</td><td>82.48</td><td>32.84</td><td>1.33</td><td>45.00</td><td>81.78</td><td>31.69</td></tr><tr><td rowspan="5">qwen2:72b</td><td>Random</td><td>1.22</td><td>29.50</td><td>84.12</td><td>33.35</td><td>1.21</td><td>34.40</td><td>87.05</td><td>43.42</td><td>1.13</td><td>46.15</td><td>85.52</td><td>37.22</td></tr><tr><td>Isometric</td><td>1.20</td><td>28.70</td><td>84.11</td><td>33.29</td><td>1.19</td><td>36.00</td><td>86.73</td><td>42.56</td><td>1.14</td><td>45.60</td><td>85.42</td><td>36.82</td></tr><tr><td>Same</td><td>1.22</td><td>31.35</td><td>83.97</td><td>32.99</td><td>1.18</td><td>37.05</td><td>87.22</td><td>43.57</td><td>1.12</td><td>48.10</td><td>85.68</td><td>37.43</td></tr><tr><td>Short</td><td>1.14</td><td>35.30</td><td>83.65</td><td>31.82</td><td>1.15</td><td>42.45</td><td>86.47</td><td>40.71</td><td>1.10</td><td>49.10</td><td>85.24</td><td>36.88</td></tr><tr><td>Tiny</td><td>1.14</td><td>35.55</td><td>83.20</td><td>30.38</td><td>1.15</td><td>43.55</td><td>85.81</td><td>39.52</td><td>1.10</td><td>50.15</td><td>84.62</td><td>35.44</td></tr><tr><td rowspan="5">qwen2:7b</td><td>Random</td><td>1.18</td><td>29.10</td><td>81.17</td><td>23.82</td><td>1.21</td><td>31.65</td><td>84.19</td><td>35.02</td><td>1.09</td><td>46.06</td><td>83.61</td><td>30.71</td></tr><tr><td>Isometric</td><td>1.17</td><td>32.05</td><td>81.31</td><td>23.71</td><td>1.19</td><td>31.20</td><td>84.19</td><td>35.11</td><td>1.09</td><td>45.35</td><td>83.40</td><td>30.67</td></tr><tr><td>Same</td><td>1.17</td><td>31.20</td><td>81.37</td><td>23.99</td><td>1.19</td><td>32.00</td><td>84.36</td><td>35.03</td><td>1.10</td><td>45.95</td><td>83.33</td><td>30.93</td></tr><tr><td>Short</td><td>1.15</td><td>33.55</td><td>80.81</td><td>23.13</td><td>1.17</td><td>35.00</td><td>83.63</td><td>34.17</td><td>1.08</td><td>47.00</td><td>83.12</td><td>30.45</td></tr><tr><td>Tiny</td><td>1.13</td><td>37.00</td><td>80.55</td><td>22.31</td><td>1.16</td><td>34.30</td><td>82.87</td><td>33.13</td><td>1.06</td><td>49.00</td><td>83.20</td><td>30.41</td></tr><tr><td>Oracle</td><td></td><td>1.07</td><td>65.00</td><td>87.74</td><td>49.60</td><td>1.05</td><td>76.50</td><td>87.99</td><td>55.60</td><td>1.03</td><td>83.00</td><td>88.47</td><td>53.50</td></tr></tbody></table>
<table><caption>Table 12: 20-shot prompting for all language pairs when sampling examples from varying pools. We report length ratio (LR), length compliance (LC), BERTScore (BS), and BLEU. All numbers are averaged across 10 instances. The prompt text *matches* the pool type.</caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">Pool Type</th><th colspan="4">En-De</th><th colspan="4">En-Fr</th><th colspan="4">En-Es</th></tr><tr><th>LR</th><th>LC</th><th>BS</th><th>BLEU</th><th>LR</th><th>LC</th><th>BS</th><th>BLEU</th><th>LR</th><th>LC</th><th>BS</th><th>BLEU</th></tr></thead><tbody><tr><td rowspan="5">gemma2:27b</td><td>Random</td><td>1.13</td><td>38.05</td><td>84.15</td><td>33.75</td><td>1.14</td><td>39.40</td><td>86.74</td><td>42.23</td><td>1.08</td><td>49.35</td><td>85.98</td><td>38.82</td></tr><tr><td>Isometric</td><td>1.13</td><td>39.70</td><td>84.08</td><td>33.24</td><td>1.14</td><td>39.60</td><td>86.71</td><td>42.21</td><td>1.08</td><td>48.90</td><td>86.05</td><td>38.70</td></tr><tr><td>Same</td><td>1.13</td><td>39.15</td><td>84.29</td><td>33.68</td><td>1.14</td><td>42.50</td><td>87.08</td><td>42.86</td><td>1.08</td><td>49.85</td><td>86.26</td><td>39.03</td></tr><tr><td>Short</td><td>1.07</td><td>43.45</td><td>83.07</td><td>30.32</td><td>1.07</td><td>45.05</td><td>85.28</td><td>37.52</td><td>1.04</td><td>52.20</td><td>85.55</td><td>37.43</td></tr><tr><td>Tiny</td><td>1.01</td><td>42.00</td><td>81.88</td><td>27.16</td><td>1.01</td><td>40.90</td><td>83.62</td><td>33.65</td><td>0.98</td><td>48.55</td><td>84.25</td><td>34.32</td></tr><tr><td rowspan="5">gemma2:9b</td><td>Random</td><td>1.14</td><td>37.30</td><td>83.81</td><td>32.08</td><td>1.15</td><td>39.60</td><td>86.86</td><td>43.01</td><td>1.09</td><td>50.72</td><td>85.82</td><td>37.94</td></tr><tr><td>Isometric</td><td>1.13</td><td>37.75</td><td>83.73</td><td>31.51</td><td>1.14</td><td>41.10</td><td>86.68</td><td>42.17</td><td>1.08</td><td>51.40</td><td>86.01</td><td>37.93</td></tr><tr><td>Same</td><td>1.12</td><td>39.80</td><td>83.72</td><td>30.97</td><td>1.14</td><td>42.40</td><td>86.79</td><td>42.11</td><td>1.07</td><td>54.05</td><td>85.97</td><td>38.00</td></tr><tr><td>Short</td><td>1.08</td><td>41.20</td><td>82.83</td><td>28.93</td><td>1.09</td><td>46.70</td><td>85.23</td><td>37.48</td><td>1.05</td><td>54.85</td><td>85.46</td><td>36.86</td></tr><tr><td>Tiny</td><td>1.04</td><td>46.70</td><td>81.94</td><td>26.43</td><td>1.02</td><td>43.85</td><td>83.56</td><td>33.87</td><td>1.00</td><td>54.35</td><td>84.64</td><td>33.62</td></tr><tr><td rowspan="5">llama3:70b</td><td>Random</td><td>1.19</td><td>27.50</td><td>85.01</td><td>35.32</td><td>1.19</td><td>30.95</td><td>87.50</td><td>44.27</td><td>1.11</td><td>47.80</td><td>85.61</td><td>36.93</td></tr><tr><td>Isometric</td><td>1.18</td><td>28.25</td><td>84.97</td><td>35.01</td><td>1.18</td><td>31.20</td><td>87.59</td><td>43.92</td><td>1.10</td><td>48.70</td><td>85.64</td><td>37.12</td></tr><tr><td>Same</td><td>1.17</td><td>28.95</td><td>85.31</td><td>35.16</td><td>1.18</td><td>32.25</td><td>87.66</td><td>44.31</td><td>1.11</td><td>48.55</td><td>85.63</td><td>36.80</td></tr><tr><td>Short</td><td>1.06</td><td>39.75</td><td>83.12</td><td>29.42</td><td>1.03</td><td>42.10</td><td>84.60</td><td>35.92</td><td>1.01</td><td>50.25</td><td>84.71</td><td>35.11</td></tr><tr><td>Tiny</td><td>0.95</td><td>34.90</td><td>80.71</td><td>23.33</td><td>0.90</td><td>37.25</td><td>81.08</td><td>26.91</td><td>0.83</td><td>37.00</td><td>81.01</td><td>25.22</td></tr><tr><td rowspan="5">llama3:8b</td><td>Random</td><td>1.13</td><td>32.40</td><td>81.50</td><td>26.26</td><td>1.13</td><td>35.20</td><td>83.19</td><td>33.89</td><td>1.06</td><td>48.35</td><td>82.46</td><td>30.69</td></tr><tr><td>Isometric</td><td>1.13</td><td>34.00</td><td>81.80</td><td>26.70</td><td>1.13</td><td>36.25</td><td>83.53</td><td>34.10</td><td>1.06</td><td>47.35</td><td>82.78</td><td>30.94</td></tr><tr><td>Same</td><td>1.14</td><td>34.25</td><td>81.96</td><td>26.40</td><td>1.14</td><td>34.15</td><td>83.86</td><td>34.56</td><td>1.07</td><td>51.05</td><td>83.11</td><td>31.16</td></tr><tr><td>Short</td><td>1.06</td><td>37.95</td><td>80.57</td><td>24.32</td><td>1.04</td><td>38.10</td><td>81.43</td><td>28.63</td><td>1.00</td><td>48.00</td><td>81.45</td><td>28.03</td></tr><tr><td>Tiny</td><td>1.00</td><td>37.10</td><td>79.11</td><td>20.94</td><td>0.97</td><td>37.05</td><td>79.69</td><td>26.43</td><td>0.90</td><td>42.25</td><td>79.13</td><td>23.72</td></tr><tr><td rowspan="5">mistral:7b</td><td>Random</td><td>1.15</td><td>33.30</td><td>80.57</td><td>23.37</td><td>1.17</td><td>36.20</td><td>82.27</td><td>30.12</td><td>1.07</td><td>49.60</td><td>82.34</td><td>28.71</td></tr><tr><td>Isometric</td><td>1.15</td><td>33.35</td><td>80.60</td><td>23.24</td><td>1.16</td><td>35.25</td><td>82.36</td><td>30.01</td><td>1.07</td><td>50.05</td><td>82.30</td><td>28.57</td></tr><tr><td>Same</td><td>1.16</td><td>33.50</td><td>80.90</td><td>23.88</td><td>1.17</td><td>35.40</td><td>82.55</td><td>29.78</td><td>1.09</td><td>49.50</td><td>82.82</td><td>29.06</td></tr><tr><td>Short</td><td>1.13</td><td>36.50</td><td>80.34</td><td>22.27</td><td>1.14</td><td>39.30</td><td>82.03</td><td>29.15</td><td>1.05</td><td>51.10</td><td>82.02</td><td>28.19</td></tr><tr><td>Tiny</td><td>1.12</td><td>39.00</td><td>79.55</td><td>21.25</td><td>1.15</td><td>39.95</td><td>80.96</td><td>28.10</td><td>1.04</td><td>51.20</td><td>81.77</td><td>27.70</td></tr><tr><td rowspan="5">mixtral:8x7b</td><td>Random</td><td>1.42</td><td>29.35</td><td>82.14</td><td>30.68</td><td>1.30</td><td>32.20</td><td>84.55</td><td>38.96</td><td>1.44</td><td>41.80</td><td>83.27</td><td>33.30</td></tr><tr><td>Isometric</td><td>1.35</td><td>32.15</td><td>82.24</td><td>31.23</td><td>1.24</td><td>33.65</td><td>84.60</td><td>38.34</td><td>1.30</td><td>44.55</td><td>83.83</td><td>34.02</td></tr><tr><td>Same</td><td>1.32</td><td>32.25</td><td>82.60</td><td>31.69</td><td>1.23</td><td>34.10</td><td>84.95</td><td>38.54</td><td>1.27</td><td>42.20</td><td>84.09</td><td>34.79</td></tr><tr><td>Short</td><td>1.33</td><td>35.65</td><td>81.56</td><td>29.40</td><td>1.22</td><td>36.85</td><td>83.80</td><td>35.92</td><td>1.27</td><td>46.90</td><td>83.64</td><td>33.99</td></tr><tr><td>Tiny</td><td>1.24</td><td>38.40</td><td>80.73</td><td>27.13</td><td>1.21</td><td>37.15</td><td>82.91</td><td>34.10</td><td>1.24</td><td>45.45</td><td>82.82</td><td>32.94</td></tr><tr><td rowspan="5">qwen2:72b</td><td>Random</td><td>1.21</td><td>30.10</td><td>84.04</td><td>33.19</td><td>1.19</td><td>34.05</td><td>87.06</td><td>43.62</td><td>1.12</td><td>45.15</td><td>85.66</td><td>37.59</td></tr><tr><td>Isometric</td><td>1.21</td><td>30.35</td><td>84.04</td><td>33.06</td><td>1.18</td><td>36.00</td><td>86.97</td><td>42.64</td><td>1.11</td><td>46.30</td><td>85.58</td><td>37.18</td></tr><tr><td>Same</td><td>1.21</td><td>31.10</td><td>84.06</td><td>33.12</td><td>1.17</td><td>35.15</td><td>87.14</td><td>43.29</td><td>1.12</td><td>48.60</td><td>85.65</td><td>37.30</td></tr><tr><td>Short</td><td>1.14</td><td>33.95</td><td>83.97</td><td>32.04</td><td>1.17</td><td>41.50</td><td>86.24</td><td>40.66</td><td>1.11</td><td>48.90</td><td>85.38</td><td>36.94</td></tr><tr><td>Tiny</td><td>1.15</td><td>36.35</td><td>83.51</td><td>31.21</td><td>1.15</td><td>42.25</td><td>85.99</td><td>40.03</td><td>1.11</td><td>48.50</td><td>84.58</td><td>35.28</td></tr><tr><td rowspan="5">qwen2:7b</td><td>Random</td><td>1.17</td><td>29.94</td><td>81.22</td><td>23.97</td><td>1.20</td><td>30.35</td><td>83.96</td><td>34.96</td><td>1.10</td><td>44.65</td><td>83.40</td><td>30.81</td></tr><tr><td>Isometric</td><td>1.17</td><td>31.15</td><td>81.27</td><td>23.54</td><td>1.19</td><td>30.45</td><td>84.01</td><td>35.03</td><td>1.10</td><td>46.35</td><td>83.60</td><td>31.22</td></tr><tr><td>Same</td><td>1.18</td><td>30.15</td><td>81.39</td><td>24.22</td><td>1.19</td><td>33.05</td><td>84.43</td><td>35.24</td><td>1.10</td><td>44.70</td><td>83.35</td><td>30.91</td></tr><tr><td>Short</td><td>1.15</td><td>33.85</td><td>80.92</td><td>23.33</td><td>1.17</td><td>33.85</td><td>83.48</td><td>34.25</td><td>1.09</td><td>46.80</td><td>83.43</td><td>30.65</td></tr><tr><td>Tiny</td><td>1.14</td><td>34.25</td><td>80.34</td><td>21.87</td><td>1.15</td><td>34.85</td><td>82.98</td><td>33.09</td><td>1.07</td><td>49.50</td><td>83.11</td><td>30.23</td></tr><tr><td>Oracle</td><td></td><td>1.07</td><td>65.00</td><td>87.74</td><td>49.60</td><td>1.05</td><td>76.50</td><td>87.99</td><td>55.60</td><td>1.03</td><td>83.00</td><td>88.47</td><td>53.50</td></tr></tbody></table>