The Translator’s Canvas: Using LLMs to Enhance Poetry Translation

Abstract

We explore the potential of LLMs to enhance the translation process of rhymed and non-rhymed poetry. We examine LLMs’ performance in terms of lexical variety, lexical density, and sentence length compared to human translations (HT). We also examine the models’ abilities to translate sonnets while preserving the rhyme scheme of the source text. Our findings suggest that LLMs can serve as valuable tools for literary translators, assisting with the creative process and suggesting solutions to problems that may not otherwise have been considered. However, if the paradigm is flipped, such that instead of the systems being as tools by human translators, humans are used to post-edit the outputs to a standard comparable to the published translations, the amount of work required to complete the post-editing stage may outweigh any benefits assocaiated with using machine translation in the first place.

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