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Machine translation benchmarks have functionally saturated, but pairing human-authored failure cases with deterministic verification rules reveals critical multimodal blind spots that automated metrics consistently miss.
Conventional wisdom about language transfer in NLP fails in the context of few-shot in-context learning, revealing the need for new heuristics in source language selection.
Luxembourgish NLU is finally getting the attention it deserves with the launch of ltzGLUE, revealing critical insights into model performance and language capabilities.
