User study with professional En-Nl translators found LLM-based error highlights and APE correction suggestions did not improve productivity or quality over standard post-editing but were better received and enhanced user experience.
(Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts
2 Pith papers cite this work. Polarity classification is still indexing.
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A literature survey that organizes prompting, fine-tuning, preference optimization, and context-aware techniques for LLM-based machine translation with emphasis on low-resource languages.
citing papers explorer
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Smarter edits? Post-editing with error highlights and translation suggestions
User study with professional En-Nl translators found LLM-based error highlights and APE correction suggestions did not improve productivity or quality over standard post-editing but were better received and enhanced user experience.
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Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
A literature survey that organizes prompting, fine-tuning, preference optimization, and context-aware techniques for LLM-based machine translation with emphasis on low-resource languages.