A Bayesian framework decomposes mLLM variance, showing language features explain 79-92% of language identity variance and that model identity vs. benchmark-model interactions dominate differently for understanding versus reasoning tasks.
MEGAVERSE : Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks
2 Pith papers cite this work. Polarity classification is still indexing.
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Parameter alignment strategies substantially reduce forgetting in family-based continual pretraining of multilingual LLMs across 32 languages with minimal impact on language acquisition.
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DEPART: DEcomposing PARiTy across Multilingual LLMs
A Bayesian framework decomposes mLLM variance, showing language features explain 79-92% of language identity variance and that model identity vs. benchmark-model interactions dominate differently for understanding versus reasoning tasks.
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Parameter Alignment Mitigates Catastrophic Forgetting in Multilingual Expert Language Models
Parameter alignment strategies substantially reduce forgetting in family-based continual pretraining of multilingual LLMs across 32 languages with minimal impact on language acquisition.