AINeutralarXiv – CS AI · 3h ago6/10
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DEPART: DEcomposing PARiTy across Multilingual LLMs
Researchers introduce DEPART, a Bayesian framework that systematically decomposes performance disparities across multilingual large language models into interpretable components. The study reveals that language features and representational similarity to English explain 79-92% of variance, with model identity dominating NLU tasks while benchmark-model interactions drive reasoning task differences.