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1 papers
Training-Free Adaptive Speculative Decoding via Linguistic Priors (2026.findings-acl)

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Challenge: Speculative decoding (SPD) is a promising technique to accelerate Large Language Models (LLMs). current approaches neglect the inherent heterogeneity of natural language and fail to distinguish between semantically-rich content and structurally-predictable syntax.
Approach: They propose a training-free framework that leverages linguistic priors to enable adaptive drafting and verification.
Outcome: The proposed framework significantly accelerates inference without additional training.

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