2 citations · 2 across the 3 of their papers we have counts for
4 papers · 1 filter
OpenCharacter: Training Customizable Role-Playing LLMs with Large-Scale Synthetic Personas
Xiaoyang Wang, Hongming Zhang, Tao Ge +3
Customizable role-playing in large language models (LLMs), also known as character generalization, is gaining increasing attention for its versatility and cost-efficiency in develo…
ParallelSpec: Parallel Drafter for Efficient Speculative Decoding
Zilin Xiao, Hongming Zhang, Tao Ge +3
Speculative decoding has proven to be an efficient solution to large language model (LLM) inference, where the small drafter predicts future tokens at a low cost, and the target mo…
Router-Tuning: A Simple and Effective Approach for Enabling Dynamic-Depth in Transformers
Shwai He, Tao Ge, Guoheng Sun +3
Traditional transformer models often allocate a fixed amount of computational resources to every input token, leading to inefficient and unnecessary computation. To address this, t…
Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding
Heming Xia, Zhe Yang, Qingxiu Dong +6
To mitigate the high inference latency stemming from autoregressive decoding in Large Language Models (LLMs), Speculative Decoding has emerged as a novel decoding paradigm for LLM…