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.
Towards Universality in Multilingual Text Rewriting
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In this work, we take the first steps towards building a universal rewriter: a model capable of rewriting text in any language to exhibit a wide variety of attributes, including styles and languages, while preserving as much of the original semantics as possible. In addition to obtaining state-of-the-art results on unsupervised translation, we also demonstrate the ability to do zero-shot sentiment transfer in non-English languages using only English exemplars for sentiment. We then show that our model is able to modify multiple attributes at once, for example adjusting both language and sentiment jointly. Finally, we show that our model is capable of performing zero-shot formality-sensitive translation.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.