Laniqo at WMT 25 General Translation Task: Self-Improved and Retrieval-Augmented Translation (2025)

Abstract: This work describes Laniqo’s submission to the constrained track of the WMT25 General MT Task. We participated in 11 translation directions. Our approach combines several techniques: fine-tuning the EuroLLM-9B-Instruct model using Contrastive Preference Optimization on a synthetic dataset, applying Retrieval-Augmented Translation with human-translated data, implementing Quality-Aware Decoding, and performing postprocessing of translations with a rule-based algorithm. We analyze the contribution of each method and report improvements at every stage of our pipeline.

## introduction in this paper, we describe laniqo's submission to the wmt 2025 general mt task. we participated in the constrained track of the shared task, which limited our system to a maximum of 20 billion total parameters. this year's task focused on the translation of document-level data sampled from four domains: news, social, literary, and speech. additionally, the provided testset contained metadata from different modalities, i.e. screenshots of posts from social media and audio recordings for the speech domain. furthermore, each testset entry contained a domain-specific prompt for large language models (llms). we based our system on the eurollm-9b-instruct 1 @xcite model. we participated in all of the translation directions supported by the model, resulting in the following 11 distinct directions: czech to german; czech to ukrainian; english to chinese; english to czech; english to estonian; english to italian; english to japanese; english to korean; english to russian; english to ukrainian; and japanese to chinese. ## related work llms have become the dominant approach in the field, overtaking smaller neural machine translation (nmt) models @xcite , particularly following the release of open-source, multilingual llms, such as eurollm and tower+ @xcite . a fundamental advantage of llms is their ability to process instructions provided directly within the prompt. studies have shown that few-shot prompting outperforms zero-shot translation and that selecting examples with high lexical similarity, employing methods such as fuzzy matching, can further enhance translation quality @xcite . quality-aware decoding (qad) @xcite is an established method for improving translation quality. it facilitates mbr decoding, which uses a translation quality metric as the scoring function to rerank a list of translation candidates. subsequent research has consistently demonstrated the effectiveness of this method in improving translation outputs @xcite . mbr decoding is computationally expensive because it requires generating numerous translation candidates and making pairwise comparisons between them. the computational cost, apparent during inference, can be reduced through mbr selfimprovement. @xcite , a technique that involves finetuning a model using outputs selected by mbr. the self-improvement process can be framed as a preference learning task. methods such as direct preference optimization (dpo) @xcite and cpo have been shown to be more effective than supervised fine-tuning (sft) for such tasks. hence, using these preference optimization methods with mbr self-improvement has been shown to yield further enhancements in terms of translation quality @xcite . nmt and llms often struggle with specific token types, such as numbers, urls, or emojis @xcite . these models may incorrectly translate or even omit such tokens. while these errors are critical for the user, they are often not captured by neural metrics. therefore, a simple post-processing step can become highly valuable. by employing a straightforward rule-based method as proposed in previous work @xcite , these errors can be detected and corrected. ## results the lack of access to reference translations, the scores were calculated in quality estimation (qe) mode, based on source texts and hypotheses only. the results presented in although the use of rat alone does not visibly enhance quality, its combination with cpo results in substantially greater gains. overall, these findings suggest that fine-tuning through cpo effectively enhanced translation quality, aligning the model's outputs more closely with human preferences as indicated by quality estimation metrics. qad yields the most significant improvements across the entire processing pipeline. we specifically noted improvements in the xcomet-xl scores. however, it is important to consider that, due to mint @xcite , xcomet-xl as an interfering metric may be biased and can't be used to evaluate the model fairly. for this reason, we also present results from other neural metrics, providing a more comprehensive assessment of translation quality. additionally, rule-based postprocessing helps to avoid translation errors, even though these improvements are not reflected in the evaluation metrics due to their limitations. moreover, we performed statistical tests using the paired bootstrap resampling method @xcite . we sampled @xmath3.4 * testset_length segments and p-value @xmath4.05. we compared the results of each pipeline step with the previous one, and the baseline to the wmt25 testset prompt solution. the results show that the baseline increase in the remedy-qe score is statistically significant compared to the results of the wmt25 testset prompts. furthermore, cpo is one of the most meaningful steps in the entire pipeline, showing a significant difference compared to the baseline according to all the considered metrics. while adding the rat step improves the results slightly, using the qad method is the second most dawid wisniewski, mikolaj pokrywka, and zofia rostek. 2025a. do not change me: on transferring entities without modification in neural machine translation -a multilingual perspective. dawid wisniewski, antoni solarski, and artur nowakowski. 2025b. exploring the feasibility of multilingual grammatical error correction with a single llm up to 9b parameters: a comparative study of 17 models. zhanglin wu, daimeng wei, zongyao li, hengchao shang, jiaxin guo, shaojun li, zhiqiang rao, yuanchang luo, ning xie, and hao yang. 2024. choose the final translation from nmt and llm hypotheses using mbr decoding: hw-tsc's submission to the wmt24 general mt shared task. in proceedings of the ninth conference on machine translation, pages 155-164, miami, florida, usa. association for computational linguistics. haoran xu, amr sharaf, yunmo chen, weiting tan, lingfeng shen, benjamin van durme, kenton murray, and young jin kim. 2024. contrastive preference optimization: pushing the boundaries of llm performance in machine translation. guangyu yang, jinghong chen, weizhe lin, and bill byrne. 2024. direct preference optimization for neural machine translation with minimum bayes risk decoding. in proceedings of the 2024 conference of the north american chapter of the association for computational linguistics: human language technologies (volume 2: short papers), pages 391-398, mexico city, mexico. association for computational linguistics.