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Adaptive layer selection in LLMs can significantly enhance inference efficiency without sacrificing accuracy, achieving better performance than static pruning methods.
Classical Chinese, with its conciseness and obscurity, unlocks a surprisingly effective attack vector against LLM safety filters, and can be automatically exploited via bio-inspired optimization.
