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Now you can retrofit any frozen language model with explicit, editable "sense knobs" for steering, disambiguation, and cross-lingual transfer, without pretraining from scratch.
Fixing your parallelism strategy while tuning batch size (or vice versa) leaves performance on the table: COPUS adaptively co-tunes both for faster LLM training.
LLMs still struggle to reason in context when cultural and linguistic nuances are involved, achieving only 44% accuracy on a new grounded benchmark spanning 14 languages.
