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README.md
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@@ -91,3 +91,20 @@ The model was evaluated by our custom script on three types of data. The results
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| GaMS-9B-Instruct | 0.693659 | 0.685006 | 0.673394 | 0.723470 | 13.50% | 4.83% | 33.15% |
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| EuroLLM-9B-Instruct | 0.689321 | 0.668084 | 0.670723 | 0.729227 | 8.97% | 1.89% | 35.08% |
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| GaMS-9B-SFT-Translator | 0.682467 | 0.676580 | 0.673650 | 0.699602 | 5.14% | 1.48% | 30.53% |
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| GaMS-9B-Instruct | 0.693659 | 0.685006 | 0.673394 | 0.723470 | 13.50% | 4.83% | 33.15% |
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| EuroLLM-9B-Instruct | 0.689321 | 0.668084 | 0.670723 | 0.729227 | 8.97% | 1.89% | 35.08% |
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| GaMS-9B-SFT-Translator | 0.682467 | 0.676580 | 0.673650 | 0.699602 | 5.14% | 1.48% | 30.53% |
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*Note* - the evaluation script and evaluation data can be found in this [github repo](https://github.com/DarioVajda/translation_dpo) under the data_pipeline folder. See the README for more detailed instructions.
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## Citation
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If you found this project useful in your work, please cite our paper with the following BibTeX citation:
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```txt
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@misc{vajda2025improvingllmsmachinetranslation,
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title={Improving LLMs for Machine Translation Using Synthetic Preference Data},
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author={Dario Vajda and Domen Vreš and Marko Robnik-Šikonja},
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year={2025},
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eprint={2508.14951},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2508.14951},
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}
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```
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