PARAMΔ upcycles dense models to MoE for per-language experts and grafts post-training deltas to enable data-efficient language expansion while preserving original capabilities.
Transactions of the Association for Computational Linguistics , volume=
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Automatic translation metrics show lower agreement with humans on unseen technical domains than humans show with each other, and their robustness claims weaken when benchmarked against inter-annotator agreement instead of raw scores.
citing papers explorer
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A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM$\Delta$ Integration into Upcycled MoE
PARAMΔ upcycles dense models to MoE for per-language experts and grafts post-training deltas to enable data-efficient language expansion while preserving original capabilities.
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Who Watches the Watchmen? Humans Disagree With Translation Metrics on Unseen Domains
Automatic translation metrics show lower agreement with humans on unseen technical domains than humans show with each other, and their robustness claims weaken when benchmarked against inter-annotator agreement instead of raw scores.