most citedLeveraging 2D Information for Long-term Time Series Forecasting with Vanilla Transformers

3 citations · 7 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2024

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…

cs.CL2024★ 1 cited

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…

cs.LG2024★ 3 cited

Leveraging 2D Information for Long-term Time Series Forecasting with Vanilla Transformers

Xin Cheng, Xiuying Chen, Shuqi Li +4

Time series prediction is crucial for understanding and forecasting complex dynamics in various domains, ranging from finance and economics to climate and healthcare. Based on Tran…

cs.LG2024★ 2 cited

What Makes Quantization for Large Language Models Hard? An Empirical Study from the Lens of Perturbation

Zhuocheng Gong, Jiahao Liu, Jingang Wang +3

Quantization has emerged as a promising technique for improving the memory and computational efficiency of large language models (LLMs). Though the trade-off between performance an…

cs.CL2023

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…

cs.CL2023

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…

cs.CL2023★ 1 cited

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…