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Lumen, the open-source model, achieves performance comparable to leading proprietary systems in both translation quality and translation-instruction following, and under an absolute 1--5 LLM-judge rubric that evaluates translation-instruction following and translation quality separately.
Experimental results across three models across both domain-specific and general benchmarks demonstrate that NGFT significantly outperforms existing mainstream fine-tuning methods in both efficiency and performance, while effectively mitigating catastrophic forgetting.
A unified reasoning reward model supporting multiple languages and evaluation paradigms, UniRRM uses a staged reasoning chain to dynamically generate task-generic and instruction-specific criteria, enabling fine-grained, input-adaptive judgments while maintaining consistency across languages.
CulturalMenuBench shows that near-perfect recognition can conceal an inability to apply cultural knowledge, motivating training that explicitly connects perception, procedure, and cultural context.
This work proposes STAR-masked Preference Optimization (StarPO), a framework that ranks document-level hypotheses by structural quality and utilizes a dynamic alignment mask to focus optimization on misaligned segments and demonstrates that StarPO significantly enhances translation quality and structural integrity.
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