SOURCE-LINKED INTELLIGENCE
STAR : Sentence Translation Alignment Rate for Document-to-Document Machine Translation
Large Language Models (LLMs) have enabled a shift from sentence-level to document-to-document (Doc2Doc) machine translation, promising improved global coherence. However, document-to-document generation in a single pass frequently suffers from structural misalignment, manifesting as sentence omissions or hallucinations that violate the core requirement of source-target correspondence. To address this, we introduce Sentence Translation Alignment Rate (STAR), an auxiliary metric that explicitly quantifies sentence-level structural fidelity. Building on this, we propose STAR-masked Preference Opt
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T14:12:45.000Z
First collected: 2026-09-21T08:32:02.028Z. This is not the publication date.