5 citations · 33 across the 40 of their papers we have counts for
15 papers · 1 filter
Continuous Speculative Decoding for Autoregressive Image Generation
Zili Wang, Zheng Zhang, Kun Ding +3
Continuous visual autoregressive (AR) models have demonstrated promising performance in image generation, but their inherently sequential nature results in slow inference speed. Sp…
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models
Siming Huang, Tianhao Cheng, J. K. Liu +16
Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems. While open-access code LLMs are in…
Draw an Audio: Leveraging Multi-Instruction for Video-to-Audio Synthesis
Qi Yang, Binjie Mao, Zili Wang +6
Foley is a term commonly used in filmmaking, referring to the addition of daily sound effects to silent films or videos to enhance the auditory experience. Video-to-Audio (V2A), as…
Post-hoc Reward Calibration: A Case Study on Length Bias
Zeyu Huang, Zihan Qiu, Zili Wang +2
Reinforcement Learning from Human Feedback aligns the outputs of Large Language Models with human values and preferences. Central to this process is the reward model (RM), which tr…
AVESFormer: Efficient Transformer Design for Real-Time Audio-Visual Segmentation
Zili Wang, Qi Yang, Linsu Shi +4
Recently, transformer-based models have demonstrated remarkable performance on audio-visual segmentation (AVS) tasks. However, their expensive computational cost makes real-time in…
Layerwise Recurrent Router for Mixture-of-Experts
Zihan Qiu, Zeyu Huang, Shuang Cheng +4
The scaling of large language models (LLMs) has revolutionized their capabilities in various tasks, yet this growth must be matched with efficient computational strategies. The Mix…
DNTextSpotter: Arbitrary-Shaped Scene Text Spotting via Improved Denoising Training
Yu Xie, Qian Qiao, Jun Gao +5
More and more end-to-end text spotting methods based on Transformer architecture have demonstrated superior performance. These methods utilize a bipartite graph matching algorithm…
AIM: Let Any Multi-modal Large Language Models Embrace Efficient In-Context Learning
Jun Gao, Qian Qiao, Ziqiang Cao +2
In-context learning (ICL) facilitates Large Language Models (LLMs) exhibiting emergent ability on downstream tasks without updating billions of parameters. However, in the area of…
GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory
Haoze Wu, Zihan Qiu, Zili Wang +2
Mixture-of-Experts (MoE) has been demonstrated as an efficient method to scale up models. By dynamically and sparsely selecting activated experts, MoE can effectively reduce comput…
A Closer Look into Mixture-of-Experts in Large Language Models
Ka Man Lo, Zeyu Huang, Zihan Qiu +2
Mixture-of-experts (MoE) is gaining increasing attention due to its unique properties and remarkable performance, especially for language tasks. By sparsely activating a subset of…
UniICL: An Efficient Unified Framework Unifying Compression, Selection, and Generation
Jun Gao, Qi Lv, Zili Wang +3
In-context learning (ICL) enhances the reasoning abilities of Large Language Models (LLMs) by prepending a few demonstrations. It motivates researchers to introduce more examples t…
Beyond Language Models: Byte Models are Digital World Simulators
Shangda Wu, Xu Tan, Zili Wang +3
Traditional deep learning often overlooks bytes, the basic units of the digital world, where all forms of information and operations are encoded and manipulated in binary format. I…
ChatMusician: Understanding and Generating Music Intrinsically with LLM
Ruibin Yuan, Hanfeng Lin, Yi Wang +32
While Large Language Models (LLMs) demonstrate impressive capabilities in text generation, we find that their ability has yet to be generalized to music, humanity's creative langua…
m2mKD: Module-to-Module Knowledge Distillation for Modular Transformers
Ka Man Lo, Yiming Liang, Wenyu Du +5
Modular neural architectures are gaining attention for their powerful generalization and efficient adaptation to new domains. However, training these models poses challenges due to…
HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts
Hao Zhao, Zihan Qiu, Huijia Wu +3
The Mixture of Experts (MoE) for language models has been proven effective in augmenting the capacity of models by dynamically routing each input token to a specific subset of expe…