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cs.CL2025

Accelerate Speculative Decoding with Sparse Computation in Verification

Jikai Wang, Jianchao Tan, Yuxuan Hu +6

Speculative decoding accelerates autoregressive language model inference by verifying multiple draft tokens in parallel. However, the verification stage often becomes the dominant…

cs.CL2025

Efficient Reasoning for LLMs through Speculative Chain-of-Thought

Jikai Wang, Juntao Li, Jianye Hou +3

Large reasoning language models such as OpenAI-o1 and Deepseek-R1 have recently attracted widespread attention due to their impressive task-solving abilities. However, the enormous…

cs.CL2025

Alignment-Augmented Speculative Decoding with Alignment Sampling and Conditional Verification

Jikai Wang, Zhenxu Tian, Juntao Li +5

Recent works have revealed the great potential of speculative decoding in accelerating the autoregressive generation process of large language models. The success of these methods…

cs.CL2024

OPT-Tree: Speculative Decoding with Adaptive Draft Tree Structure

Jikai Wang, Yi Su, Juntao Li +5

Autoregressive language models demonstrate excellent performance in various scenarios. However, the inference efficiency is limited by its one-step-one-word generation mode, which…

cs.CL2024

OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning

Dan Qiao, Yi Su, Pinzheng Wang +18

Large Language Models (LLMs) have played an important role in many fields due to their powerful capabilities.However, their massive number of parameters leads to high deployment re…