VocabTailor introduces a decoupled dynamic vocabulary selection framework that reduces vocabulary-related memory in SLMs by up to 99% with minimal task performance loss.
2502.12404 , archivePrefix=
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HardMTBench is a difficulty-aware benchmark of 20,000 directional test items across 12 domains that widens GEMBA score ranges by a factor of two and reveals domain-specific weaknesses in 22 MT systems.
RouteLMT learns to route MT requests to large or small LLMs by predicting marginal quality gain from small-model token representations, yielding a better quality-budget Pareto frontier than baselines.
CHORUS multi-agent system reduced professional translation time by 33.8% while lowering cognitive effort and raising BLEU/COMET scores in a 30-participant within-subject study.
Combines GRPO with teacher-guided on-policy distillation and introduces LongBlocks dataset to yield more stable long-context reasoning than either method alone.
Hy-MT2 presents three new multilingual translation models that claim to outperform listed open-source and commercial systems on diverse tasks while enabling low-storage on-device use.
EXAONE 4.5 is a new open-weight multimodal model that matches general benchmarks and outperforms similar-scale models on document understanding and Korean contextual reasoning.
citing papers explorer
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VocabTailor: Dynamic Vocabulary Selection for Downstream Tasks in Small Language Models
VocabTailor introduces a decoupled dynamic vocabulary selection framework that reduces vocabulary-related memory in SLMs by up to 99% with minimal task performance loss.
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HardMTBench: Stress-Testing Chinese-English Translation on Knowledge-Intensive Domains
HardMTBench is a difficulty-aware benchmark of 20,000 directional test items across 12 domains that widens GEMBA score ranges by a factor of two and reveals domain-specific weaknesses in 22 MT systems.
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RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment
RouteLMT learns to route MT requests to large or small LLMs by predicting marginal quality gain from small-model token representations, yielding a better quality-budget Pareto frontier than baselines.
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CHORUS: Effort-Aware Multi-Agent Human-AI Collaboration for Professional Translation
CHORUS multi-agent system reduced professional translation time by 33.8% while lowering cognitive effort and raising BLEU/COMET scores in a 30-participant within-subject study.
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A Recipe for Long-Context Reasoning in Large Language Models via On-Policy Optimization and Distillation
Combines GRPO with teacher-guided on-policy distillation and introduces LongBlocks dataset to yield more stable long-context reasoning than either method alone.
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Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild
Hy-MT2 presents three new multilingual translation models that claim to outperform listed open-source and commercial systems on diverse tasks while enabling low-storage on-device use.
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EXAONE 4.5 Technical Report
EXAONE 4.5 is a new open-weight multimodal model that matches general benchmarks and outperforms similar-scale models on document understanding and Korean contextual reasoning.