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The paper introduces ConceptGuard, a benchmark for evaluating context-sensitive unlearning in large language models using dual-use concepts, revealing that current unlearning techniques perform poorly under this practical evaluation framework.
MicroSpec is a training-free technique that builds compact, context-sensitive vocabularies on-the-fly to accelerate speculative decoding in large language models, reducing average vocabulary size by over 40x and achieving up to 1.32x end-to-end speedup over EAGLE-2.