Papers by Dogyeong Yun

1 papers
Speculative Verification: Exploiting Information Gain for Speculative Decoding (2026.findings-acl)

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Challenge: Large Language Models (LLMs) are used for many applications but their size and computational cost make inference serving a significant challenge.
Approach: They propose an efficient augmentation to Speculative Decoding (SD) that predicts speculation accuracy and dynamically adapts the verification length to maximize throughput.
Outcome: The proposed model reduces wasted verification on rejected tokens and improves decoding efficiency.

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