most citedResponse Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline

9 citations · 19 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

Junsong Chen, Chongjian Ge, Enze Xie +7

In this paper, we introduce PixArt-Σ, a Diffusion Transformer model~(DiT) capable of directly generating images at 4K resolution. PixArt-Σrepresents a significant advancement over…

cs.AI20243 cited

A Survey of Reasoning with Foundation Models

Jiankai Sun, Chuanyang Zheng, Enze Xie +31

Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. It…

cs.CL2023

CAME: Confidence-guided Adaptive Memory Efficient Optimization

Yang Luo, Xiaozhe Ren, Zangwei Zheng +3

Adaptive gradient methods, such as Adam and LAMB, have demonstrated excellent performance in the training of large language models. Nevertheless, the need for adaptivity requires m…

cs.CL20239 cited

Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline

Zangwei Zheng, Xiaozhe Ren, Fuzhao Xue +3

Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks. However, the inference process for LLMs comes with sign…

cs.CL20237 cited

PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing

Xiaozhe Ren, Pingyi Zhou, Xinfan Meng +14

The scaling of large language models has greatly improved natural language understanding, generation, and reasoning. In this work, we develop a system that trained a trillion-param…