LLMs systematically reduce politeness, intimacy, and formality variation in translation, and a retrieval-augmented prompting method that supplies native style exemplars improves style alignment without hurting content quality on GPT-4.
Multi-Task Neural Models for Translating Between Styles Within and Across Languages
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abstract
Generating natural language requires conveying content in an appropriate style. We explore two related tasks on generating text of varying formality: monolingual formality transfer and formality-sensitive machine translation. We propose to solve these tasks jointly using multi-task learning, and show that our models achieve state-of-the-art performance for formality transfer and are able to perform formality-sensitive translation without being explicitly trained on style-annotated translation examples.
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Towards Style Alignment in Cross-Cultural Translation
LLMs systematically reduce politeness, intimacy, and formality variation in translation, and a retrieval-augmented prompting method that supplies native style exemplars improves style alignment without hurting content quality on GPT-4.