Paper page - AI translation of literary texts is “fine”, but readers still prefer human translations

Source: https://huggingface.co/papers/2606.26040

Abstract

Human readers prefer human-translated literary works over machine translations, finding the latter less immersive and harder to distinguish from human translations, despite machine translation metrics favoring the automated versions.

AI translation of literary works is increasingly common. While the content may be rendered adequately, we do not know enough about how readers experience it in terms of immersiveness and literary effect, aspects poorly captured by automaticmachine translationmetrics or human evaluation targeting fluency and adequacy. We ask 15 avid readers to compare recently publishedhuman translations (HT) tomachine translations (MT) generated with an agenticlarge language model(LLM)-based pipeline, for 15 recent novels in French, Polish, and Japanese and translated into English. Readers evaluated approximately 8K-word excerpts in two conditions:immersive readingof the whole excerpt (30 comparisons) andclose readingof 386 aligned HT-MT chunk pairs (772 comparisons), with two readers per book and in alternating order of presentation. Overall, readers find MT “fine”, but prefer HT (slightly at excerpt-level 19/30, more clearly at chunk-level 522/772) for its ease, clarity, and immersive nature. Readers’ highlights show that MT’s quality varies more within one book than HT’s does. Crucially, readers cannot reliably tell the two apart (17/30 guess correctly) and tend to prefer the version they believe to be human. Automatic metrics, includingLLM-as-a-judgeapproaches, fail to recover reader preferences and favor MT. We release LAIT (Literary AI Translation), areader-centered evaluationdataset with 1K reader comments, 2K judgments and preference ratings, and 7.2K span-level annotations, along with our evaluation protocol and supporting interface.

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