Jesujoba Oluwadara Alabi, Israel Abebe Azime, Miaoran Zhang, Cristina España-Bonet, Rachel Bawden, Dawei Zhu, David Ifeoluwa Adelani, Clement Oyeleke Odoje, Idris Akinade, Iffat Maab, Davis David, Shamsuddeen Hassan Muhammad, Neo Putini, David O. Ademuyiwa, Andrew Caines, Dietrich Klakow. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Traditionally, success in multilingual machine translation can be attributed to three key factors in training data: large volume, diverse translation directions, and high quality.In the current practice of fine-tuning large language models (LLMs) for translation, we revisit the importance of these factors.We find that LLMs display strong translation capability after being fine-tuned on as few as 32 parallel sentences and that fine-tuning on a single translation direction enables translation in multiple directions.However, the choice of direction is critical: fine-tuning LLMs with only English on the target side can lead to task misinterpretation, which hinders translation into non-English languages.Problems also arise when noisy synthetic data is placed on the target side, especially when the target language is wellrepresented in LLM pre-training.Yet interestingly, synthesized data in an under-represented language has a less pronounced effect.Our findings suggest that when adapting LLMs to translation, the requirement on data quantity can be eased but careful considerations are still crucial to prevent an LLM from exploiting unintended data biases.

Miaoran Zhang, Marius Mosbach, David Adelani, Michael Hedderich, Dietrich Klakow. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Documents as short as a single sentence may inadvertently reveal sensitive information about their authors, including e.g.their gender or ethnicity.Style transfer is an effective way of transforming texts in order to remove any information that enables author profiling.However, for a number of current state-of-theart approaches the improved privacy is accompanied by an undesirable drop in the downstream utility of the transformed data.In this paper, we propose a simple, zero-shot way to effectively lower the risk of author profiling through multilingual back-translation using off-the-shelf translation models.We compare our models with five representative text style transfer models on three datasets across different domains.Results from both an automatic and a human evaluation show that our approach achieves the best overall performance while requiring no training data.We are able to lower the adversarial prediction of gender and race by up to 22% while retaining 95% of the original utility on downstream tasks.