1.4k citations · 4.2k across the 73 of their papers we have counts for
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When, What, and How: Rethinking Retrieval-Enhanced Speculative Decoding
Min Fang, Zhihui Fu, Qibin Zhao +1
Speculative decoding (SD) has emerged as an effective technique to accelerate large language model (LLM) inference without compromising output quality. However, the achievable spee…
DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding
Guanghao Li, Zhihui Fu, Min Fang +4
As large language models (LLMs) scale up, accuracy improves, but the autoregressive (AR) nature of decoding increases latency since each token requires a serial forward pass. Specu…
Large Language Model Sentinel: LLM Agent for Adversarial Purification
Guang Lin, Toshihisa Tanaka, Qibin Zhao
Over the past two years, the use of large language models (LLMs) has advanced rapidly. While these LLMs offer considerable convenience, they also raise security concerns, as LLMs a…
EpilepsyLLM: Domain-Specific Large Language Model Fine-tuned with Epilepsy Medical Knowledge
Xuyang Zhao, Qibin Zhao, Toshihisa Tanaka
With large training datasets and massive amounts of computing sources, large language models (LLMs) achieve remarkable performance in comprehensive and generative ability. Based on…