2 citations · 3 across the 3 of their papers we have counts for
5 papers · 1 filter
Graph-Structured Speculative Decoding
Zhuocheng Gong, Jiahao Liu, Ziyue Wang +5
Speculative decoding has emerged as a promising technique to accelerate the inference of Large Language Models (LLMs) by employing a small language model to draft a hypothesis sequ…
Towards Detecting LLMs Hallucination via Markov Chain-based Multi-agent Debate Framework
Xiaoxi Sun, Jinpeng Li, Yan Zhong +2
The advent of large language models (LLMs) has facilitated the development of natural language text generation. It also poses unprecedented challenges, with content hallucination e…
Improving Input-label Mapping with Demonstration Replay for In-context Learning
Zhuocheng Gong, Jiahao Liu, Qifan Wang +4
In-context learning (ICL) is an emerging capability of large autoregressive language models where a few input-label demonstrations are appended to the input to enhance the model's…
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression
Jiduan Liu, Jiahao Liu, Qifan Wang +5
Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these mode…
SCALE: Synergized Collaboration of Asymmetric Language Translation Engines
Xin Cheng, Xun Wang, Tao Ge +4
In this paper, we introduce SCALE, a collaborative framework that connects compact Specialized Translation Models (STMs) and general-purpose Large Language Models (LLMs) as one uni…